Book cover

Good Research Practice in Non-Clinical Pharmacology and Biomedicine pp 81–100 Cite as

Blinding and Randomization

  • Anton Bespalov 13 , 14 ,
  • Karsten Wicke 15 &
  • Vincent Castagné 16  
  • Open Access
  • First Online: 07 November 2019

33k Accesses

21 Citations

53 Altmetric

Part of the Handbook of Experimental Pharmacology book series (HEP,volume 257)

Most, if not all, guidelines, recommendations, and other texts on Good Research Practice emphasize the importance of blinding and randomization. There is, however, very limited specific guidance on when and how to apply blinding and randomization. This chapter aims to disambiguate these two terms by discussing what they mean, why they are applied, and how to conduct the acts of randomization and blinding. We discuss the use of blinding and randomization as the means against existing and potential risks of bias rather than a mandatory practice that is to be followed under all circumstances and at any cost. We argue that, in general, experiments should be blinded and randomized if (a) this is a confirmatory research that has a major impact on decision-making and that cannot be readily repeated (for ethical or resource-related reasons) and/or (b) no other measures can be applied to protect against existing and potential risks of bias.

  • Good Research Practice
  • Research rigor
  • Risks of bias

Download chapter PDF

‘When I use a word,’ Humpty Dumpty said in rather a scornful tone, ‘it means just what I choose it to mean – neither more nor less.’
Lewis Carroll ( 1871 )
Through the Looking-Glass, and What Alice Found There

1 Randomization and Blinding: Need for Disambiguation

In various fields of science, outcome of the experiments can be intentionally or unintentionally distorted if potential sources of bias are not properly controlled. There is a number of recognized risks of bias such as selection bias, performance bias, detection bias, attrition bias, etc. (Hooijmans et al. 2014 ). Some sources of bias can be efficiently controlled through research rigor measures such as randomization and blinding.

Existing guidelines and recommendations assign a significant value to adequate control over various factors that can bias the outcome of scientific experiments (chapter “Guidelines and Initiatives for Good Research Practice”). Among internal validity criteria, randomization and blinding are two commonly recognized bias-reducing instruments that need to be considered when planning a study and are to be reported when the study results are disclosed in a scientific publication.

For example, editorial policy of the Nature journals requires authors in the life sciences field to submit a checklist along with the manuscripts to be reviewed. This checklist has a list of items including questions on randomization and blinding. More specifically, for randomization, the checklist is asking for the following information: “If a method of randomization was used to determine how samples/animals were allocated to experimental groups and processed, describe it.” Recent analysis by the NPQIP Collaborative group indicated that only 11.2% of analyzed publications disclosed which method of randomization was used to determine how samples or animals were allocated to experimental groups (Macleod, The NPQIP Collaborative Group 2017 ). Meanwhile, the proportion of studies mentioning randomization was much higher – 64.2%. Do these numbers suggest that authors strongly motivated to have their work published in a highly prestigious scientific journal ignore the instructions? It is more likely that, for many scientists (authors, editors, reviewers), a statement such as “subjects were randomly assigned to one of the N treatment conditions” is considered to be sufficient to describe the randomization procedure.

For the field of life sciences, and drug discovery in particular, the discussion of sources of bias, their impact, and protective measures, to a large extent, follows the examples from the clinical research (chapter “Learning from Principles of Evidence-Based Medicine to Optimize Nonclinical Research Practices”). However, clinical research is typically conducted by research teams that are larger than those involved in basic and applied preclinical work. In the clinical research teams, there are professionals (including statisticians) trained to design the experiments and apply bias-reducing measures such as randomization and blinding. In contrast, preclinical experiments are often designed, conducted, analyzed, and reported by scientists lacking training or access to information and specialized resources necessary for proper administration of bias-reducing measures.

As a result, researchers may design and apply procedures that reflect their understanding of what randomization and blinding are. These may or may not be the correct procedures. For example, driven by a good intention to randomize 4 different treatment conditions (A, B, C, and D) applied a group of 16 mice, a scientist may design the experiment in the following way (Table 1 ).

The above example is a fairly common practice to conduct “randomization” in a simple and convenient way. Another example of common practice is, upon animals’ arrival, to pick them haphazardly up from the supplier’s transport box and place into two (or more) cages which then constitute the control and experimental group(s). However, both methods of assigning subjects to experimental treatment conditions violate the randomness principle (see below) and, therefore, should not be reported as randomization.

Similarly, the use of blinding in experimental work typically cannot be described solely by stating that “experimenters were blinded to the treatment conditions.” For both randomization and blinding, it is essential to provide details on what exactly was applied and how.

The purpose of this chapter is to disambiguate these two terms by discussing what they mean, why they are applied, and how to conduct the acts of randomization and blinding. We discuss the use of blinding and randomization as the means against existing and potential risks of bias rather than a mandatory practice that is to be followed under all circumstances and at any cost.

2 Randomization

Randomization can serve several purposes that need to be recognized individually as one or more of them may become critical when considering study designs and conditions exempt from the randomization recommendation.

First, randomization permits the use of probability theory to express the likelihood of chance as a source for the difference between outcomes. In other words, randomization enables the application of statistical tests that are common in biology and pharmacology research. For example, the central limit theorem states that the sampling distribution of the mean of any independent, random variable will be normal or close to normal, if the sample size is large enough. The central limit theorem assumes that the data are sampled randomly and that the sample values are independent of each other (i.e., occurrence of one event has no influence on the next event). Usually, if we know that subjects or items were selected randomly, we can assume that the independence assumption is met. If the study results are to be subjected to conventional statistical analyses dependent on such assumptions, adequate randomization method becomes a must.

Second, randomization helps to prevent a potential impact of the selection bias due to differing baseline or confounding characteristics of the subjects. In other words, randomization is expected to transform any systematic effects of an uncontrolled factor into a random, experimental noise. A random sample is one selected without bias: therefore, the characteristics of the sample should not differ in any systematic or consistent way from the population from which the sample was drawn. But random sampling does not guarantee that a particular sample will be exactly representative of a population. Some random samples will be more representative of the population than others. Random sampling does ensure, however, that, with a sufficiently large number of subjects, the sample becomes more representative of the population.

There are characteristics of the subjects that can be readily assessed and controlled (e.g., by using stratified randomization, see below). But there are certainly characteristics that are not known and for which randomization is the only way to control their potentially confounding influence. It should be noted, however, that the impact of randomization can be limited when the sample size is low. Footnote 1 This needs to be kept in mind given that most nonclinical studies are conducted using small sample sizes. Thus, when designing nonclinical studies, one should invest extra efforts into analysis of possible confounding factors or characteristics in order to judge whether or not experimental and control groups are similar before the start of the experiment.

Third, randomization interacts with other means to reduce risks of bias. Most importantly, randomization is used together with blinding to conceal the allocation sequence. Without an adequate randomization procedure, efforts to introduce and maintain blinding may not always be fully successful.

2.1 Varieties of Randomization

There are several randomization methods that can be applied to study designs of differing complexities. The tools used to apply these methods range from random number tables to specialized software. Irrespective of the tools used, reporting on the randomization schedule applied should also answer the following two questions:

Is the randomization schedule based on an algorithm or a principle that can be written down and, based on the description, be reapplied by anyone at a later time point resulting in the same group composition? If yes, we are most likely dealing with a “pseudo-randomization” (e.g., see below comments about the so-called Latin square design).

Does the randomization schedule exclude any subjects and groups that belong to the experiment? If yes, one should be aware of the risks associated with excluding some groups or subjects such as a positive control group (see chapter “Out of Control? Managing Baseline Variability in Experimental Studies with Control Groups”).

An answer “yes” to either of the above questions does not automatically mean that something incorrect or inappropriate is being done. In fact, a scientist may take a decision well justified by their experience with and need of particular experimental situation. However, in any case, the answer “yes” to either or both of the questions above mandates the complete and transparent description of the study design with the subject allocation schedule.

2.1.1 Simple Randomization

One of the common randomization strategies used for between-subject study designs is called simple (or unrestricted) randomization. Simple random sampling is defined as the process of selecting subjects from a population such that just the following two criteria are satisfied:

The probability of assignment to any of the experimental groups is equal for each subject.

The assignment of one subject to a group does not affect the assignment of any other subject to that same group.

With simple randomization, a single sequence of random values is used to guide assignment of subjects to groups. Simple randomization is easy to perform and can be done by anyone without a need to involve professional statistical help. However, simple randomization can be problematic for studies with small sample sizes. In the example below, 16 subjects had to be allocated to 4 treatment conditions. Using Microsoft Excel’s function RANDBETWEEN (0.5;4.5), there were 16 random integer numbers from 1 to 4 generated. Obviously, this method has resulted in an unequal number of subjects among groups (e.g., there is only one subject assigned to group 2). This problem may occur irrespective of whether one uses machine-generated random numbers or simply tosses a coin.

An alternative approach would be to generate a list of all treatments to be administered (top row in the table below) and generate a list of random numbers (as many as the total number of subjects in a study) using a Microsoft Excel’s function RAND() that returns random real numbers greater than or equal to 0 and less than 1 (this function requires no argument):

The next step would be to sort the treatment row based on the values in the random number row (in an ascending or descending manner) and add a Subject ID row:

There is an equal number of subjects (four) assigned to each of the four treatment conditions, and the assignment is random. This method can also be used when group sizes are not equal (e.g., when a study is conducted with different numbers of genetically modified animals and animals of wild type).

However, such randomization schedule may still be problematic for some types of experiments. For example, if the subjects are tested one by one over the course of 1 day, the first few subjects could be tested in the morning hours while the last subjects – in the afternoon. In the example above, none of the first eight subjects is assigned to group 1, while the second half does not include any subject from group 3. To avoid such problems, block randomization may be applied.

2.1.2 Block Randomization

Blocking is used to supplement randomization in situations such as the one described above – when one or more external factors change or may change during the period when the experiment is run. Blocks are balanced with predetermined group assignments, which keeps the numbers of subjects in each group similar at all times. All blocks of one experiment have equal size, and each block represents all independent variables that are being studied in the experiment.

The first step in block randomization is to define the block size. The minimum block size is the number obtained by multiplying numbers of levels of all independent variables. For example, an experiment may compare the effects of a vehicle and three doses of a drug in male and female rats. The minimum block size in such case would be eight rats per block (i.e., 4 drug dose levels × 2 sexes). All subjects can be divided into N blocks of size X∗Y, where X is a number of groups or treatment conditions (i.e., 8 for the example given) and Y – number of subjects per treatment condition per block. In other words, there may be one or more subjects per treatment condition per block so that the actual block size is multiple of a minimum block size (i.e., 8, 16, 24, and so for the example given above).

The second step is, after block size has been determined, to identify all possible combinations of assignment within the block. For instance, if the study is evaluating effects of a drug (group A) or its vehicle (group B), the minimum block size is equal to 2. Thus, there are just two possible treatment allocations within a block: (1) AB and (2) BA. If the block size is equal to 4, there is a greater number of possible treatment allocations: (1) AABB, (2) BBAA, (3) ABAB, (4) BABA, (5) ABBA, and (6) BAAB.

The third step is to randomize these blocks with varying treatment allocations:

And, finally, the randomized blocks can be used to determine the subjects’ assignment to the groups. In the example above, there are 6 blocks with 4 treatment conditions in each block, but this does not mean that the experiment must include 24 subjects. This random sequence of blocks can be applied to experiments with a total number of subjects smaller or greater than 24. Further, the total number of subjects does not have to be a multiple of 4 (block size) as in the example below with a total of 15 subjects:

It is generally recommended to blind the block size to avoid any potential selection bias. Given the low sample sizes typical for preclinical research, this recommendation becomes a mandatory requirement at least for confirmatory experiments (see chapter “Resolving the Tension Between Exploration and Confirmation in Preclinical Biomedical Research”).

2.1.3 Stratified Randomization

Simple and block randomization are well suited when the main objective is to balance the subjects’ assignment to the treatment groups defined by the independent variables whose impact is to be studied in an experiment. With sample sizes that are large enough, simple and block randomization may also balance the treatment groups in terms of the unknown characteristics of the subjects. However, in many experiments, there are baseline characteristics of the subjects that do get measured and that may have an impact on the dependent (measured) variables (e.g., subjects’ body weight). Potential impact of such characteristics may be addressed by specifying inclusion/exclusion criteria, by including them as covariates into a statistical analysis, and (or) may be minimized by applying stratified randomization schedules.

It is always up to a researcher to decide where there are such potentially impactful covariates that need to be controlled and what is the best way of dealing with them. In case of doubt, the rule of thumb is to avoid any risk, apply stratified randomization, and declare an intention to conduct a statistical analysis that will isolate a potential contribution of the covariate(s).

It is important to acknowledge that, in many cases, information about such covariates may not be available when a study is conceived and designed. Thus, a decision to take covariates into account often affects the timing of getting the randomization conducted. One common example of such a covariate is body weight. A study is planned, and sample size is estimated before the animals are ordered or bred, but the body weights will not be known until the animals are ready. Another example is the size of the tumors that are inoculated and grow at different rates for a pre-specified period of time before the subjects start to receive experimental treatments.

For most situations in preclinical research, an efficient way to conduct stratified randomization is to run simple (or block) randomization several times (e.g., 100 times) and, for each iteration, calculate means for the covariate per each group (e.g., body weights for groups A and B in the example in previous section). The randomization schedule that yields the lowest between-group difference for the covariate would then be chosen for the experiment. Running a large number of iterations does not mean saving excessively large volumes of data. In fact, several tools used to support randomization allow to save the seed for the random number generator and re-create the randomization schedule later using this seed value.

Although stratified randomization is a relatively simple technique that can be of great help, there are some limitations that need to be acknowledged. First, stratified randomization can be extended to two or more stratifying variables. However, given the typically small sample sizes of preclinical studies, it may become complicated to implement if many covariates must be controlled. Second, stratified randomization works only when all subjects have been identified before group assignment. While this is often not a problem in preclinical research, there may be situations when a large study sample is divided into smaller batches that are taken sequentially into the study. In such cases, more sophisticated procedures such as the covariate adaptive randomization may need to be applied similar to what is done in clinical research (Kalish and Begg 1985 ). With this method, subjects are assigned to treatment groups by taking into account the specific covariates and assignments of subjects that have already been allocated to treatment groups. We intentionally do not provide any further examples or guidance on such advanced randomization methods as they should preferably be developed and applied in consultation with or by biostatisticians.

2.1.4 The Case of Within-Subject Study Designs

The above discussion on the randomization schedules referred to study designs known as between-subject. A different approach would be required if a study is designed as within-subject. In such study designs also known as the crossover, subjects may be given sequences of treatments with the intent of studying the differences between the effects produced by individual treatments. One should keep in mind that such sequence of testing always bears the danger that the first test might affect the following ones. If there are reasons to expect such interference, within-subjects designs should be avoided.

In the simplest case of a crossover design, there are only two treatments and only two possible sequences to administer these treatments (e.g., A-B and B-A). In nonclinical research and, particularly, in pharmacological studies, there is a strong trend to include at least three doses of a test drug and its vehicle. A Latin square design is commonly used to allocate subjects to treatment conditions. Latin square is a very simple technique, but it is often applied in a way that does not result in a proper randomization (Table 2 ).

In this example, each subject receives each of the four treatments over four consecutive study periods, and, for any given study period, each treatment is equally represented. If there are more than four subjects participating in a study, then the above schedule is copied as many times as need to cover all study subjects.

Despite its apparent convenience (such schedules can be generated without any tools), resulting allocation schedules are predictable and, what is even worse, are not balanced with respect to first-order carry-over effects (e.g., except for the first test period, D comes always after C). Therefore, such Latin square designs are not an example of properly conducted randomization.

One solution would be to create a complete set of orthogonal Latin Squares. For example, when the number of treatments equals three, there are six (i.e., 3!) possible sequences – ABC, ACB, BAC, BCA, CAB, and CBA. If the sample size is a multiple of six, then all six sequences would be applied. As the preclinical studies typically involve small sample sizes, this approach becomes problematic for larger numbers of treatments such as 4, where there are already 24 (i.e., 4!) possible sequences.

The Williams design is a special case of a Latin square where every treatment follows every other treatment the same number of times (Table 3 ).

The Williams design maintains all the advantages of the Latin square but is balanced (see Jones and Kenward 2003 for a detailed discussion on the Williams squares including the generation algorithms). There are six Williams squares possible in case of four treatments. Thus, if there are more than four subjects, more than one Williams square would be applied (e.g., two squares for eight subjects).

Constructing the Williams squares is not a randomization yet. In studies based on within-subject designs, subjects are not randomized to treatment in the same sense as they are in the between-subject design. For a within-subject design, the treatment sequences are randomized. In other words, after the Williams squares are constructed and selected, individual sequences are randomly assigned to the subjects.

2.2 Tools to Conduct Randomization

The most common and basic method of simple randomization is flipping a coin. For example, with two treatment groups (control versus treatment), the side of the coin (i.e., heads, control; tails, treatment) determines the assignment of each subject. Other similar methods include using a shuffled deck of cards (e.g., even, control; odd, treatment), throwing a dice (e.g., below and equal to 3, control; over 3, treatment), or writing numbers of pieces of paper, folding them, mixing, and then drawing one by one. A random number table found in a statistics book, online random number generators ( random.org or randomizer.org ), or computer-generated random numbers (e.g., using Microsoft Excel) can also be used for simple randomization of subjects. As explained above, simple randomization may result in an unbalanced design, and, therefore, one should pay attention to the number of subjects assigned to each treatment group. But more advanced randomization techniques may require dedicated tools and, whenever possible, should be supported by professional biostatisticians.

Randomization tools are typically included in study design software, and, for in vivo research, the most noteworthy example is the NC3Rs’ Experimental Design Assistant ( www.eda.nc3rs.org.uk ). This freely available online resource allows to generate and share a spreadsheet with the randomized allocation report after the study has been designed (i.e., variables defined, sample size estimated, etc.). Similar functionality may be provided by Electronic Laboratory Notebooks that integrate study design support (see chapter “Electronic Lab Notebooks and Experimental Design Assistants”).

Randomization is certainly supported by many data analysis software packages commonly used in research. In some cases, there is even a free tool that allows to conduct certain types of randomization online (e.g., QuickCalcs at www.graphpad.com/quickcalcs/randMenu/ ).

Someone interested to have a nearly unlimited freedom in designing and executing different types of randomization will benefit from the resources generated by the R community (see https://paasp.net/resource-center/r-scripts/ ). Besides being free and supported by a large community of experts, R allows to save the scripts used to obtain randomization schedules (along with the seed numbers) that makes the overall process not only reproducible and verifiable but also maximally transparent.

2.3 Randomization: Exceptions and Special Cases

Randomization is not and should never be seen as a goal per se. The goal is to minimize the risks of bias that may affect the design, conduct, and analysis of a study and to enable application of other research methods (e.g., certain statistical tests). Randomization is merely a tool to achieve this goal.

If not dictated by the needs of data analysis or the intention to implement blinding, in some cases, pseudo-randomizations such as the schedules described in Tables 1 and 2 may be sufficient. For example, animals delivered by a qualified animal supplier come from large batches where the breeding schemes themselves help to minimize the risk of systematic differences in baseline characteristics. This is in contrast to clinical research where human populations are generally much more heterogeneous than populations of animals typically used in research.

Randomization becomes mandatory in case animals are not received from major suppliers, are bred in-house, are not standard animals (i.e., transgenic), or when they are exposed to an intervention before the initiation of a treatment. Examples of intervention may be surgery, administration of a reagent substance inducing long-term effects, grafts, or infections. In these cases, animals should certainly be randomized after the intervention.

When planning a study, one should also consider the risk of between-subject cross-contamination that may affect the study outcome if animals receiving different treatment(s) are housed within the same cage. In such cases, the most optimal approach is to reduce the number of subjects per cage to a minimum that is acceptable from the animal care and use perspective and adjust the randomization schedule accordingly (i.e., so that all animals in the cage receive the same treatment).

There are situations when randomization becomes impractical or generates other significant risks that outweigh its benefits. In such cases, it is essential to recognize the reasons why randomization is applied (e.g., ability to apply certain statistical tests, prevention of selection bias, and support of blinding). For example, for an in vitro study with multi-well plates, randomization is usually technically possible, but one would need to recognize the risk of errors introduced during manual pipetting into a 96- or 384-well plate. With proper controls and machine-read experimental readout, the risk of bias in such case may not be seen as strong enough to accept the risk of a human error.

Another common example is provided by studies where incremental drug doses or concentrations are applied during the course of a single experiment involving just one subject. During cardiovascular safety studies, animals receive first an infusion of a vehicle (e.g., over a period of 30 min), followed by the two or three concentrations of the test drug, and the hemodynamics is being assessed along with the blood samples taken. As the goal of such studies is to establish concentration-effect relationships, one has no choice but to accept the lack of randomization. The only alternatives would be to give up on the within-subject design or conduct the study over many days to allow enough time to wash the drug out between the test days. Needless to say, neither of these options is perfect for a study where the baseline characteristics are a critical factor in keeping the sample size low. In this example, the desire to conduct a properly randomized study comes into a conflict with ethical considerations.

A similar design is often used in electrophysiological experiments (in vitro or ex vivo) where a test system needs to be equilibrated and baselined for extended periods of time (sometimes hours) to allow subsequent application of test drugs (at ascending concentrations). Because a washout cannot be easily controlled, such studies also do not follow randomized schedules of testing various drug doses.

The low-throughput studies such as in electrophysiology typically go over many days, and every day there is a small number of subjects or data points added. While one may accept the studies being not randomized in some cases, it is important to stress that there should be other measures in place that control potential sources of bias. It is a common but usually unacceptable practice to analyze the results each time a new data point has been added in order to decide whether a magic P value sank below 0.05 and the experiment can stop. For example, in one recent publication, it was stated: “For optogenetic activation experiments, cell-type-specific ablation experiments, and in vivo recordings (optrode recordings and calcium imaging), we continuously increased the number of animals until statistical significance was reached to support our conclusions.” Such an approach should be avoided by clear experimental planning and definition of study endpoints.

The above examples are provided only to illustrate that there may be special cases when randomization may not be done. This is usually not an easy decision to make and even more difficult to defend later. Therefore, one should always be advised to seek a professional advice (i.e., interaction with the biostatisticians or colleagues specializing in the risk assessment and study design issues). Needless to say, this advice should be obtained before the studies are conducted.

In the ideal case, once the randomization was applied to allocate subjects to treatment conditions, the randomization should be maintained through the study conduct and analysis to control against potential performance and outcome detection bias, respectively. In other words, it would not be appropriate first to assign the subjects, for example, to groups A and B and then do all experimental manipulations first with the group A and then with the group B.

In clinical research, blinding and randomization are recognized as the most important design techniques for avoiding bias (ICH Harmonised Tripartite Guideline 1998 ; see also chapter “Learning from Principles of Evidence-Based Medicine to Optimize Nonclinical Research Practices”). In the preclinical domain, there is a number of instruments assessing risks of bias, and the criteria most often included are randomization and blinding (83% and 77% of a total number of 30 instruments analyzed, Krauth et al. 2013 ).

While randomization and blinding are often discussed together and serve highly overlapping objectives, attitude towards these two research rigor measures is strikingly different. The reason for a higher acceptance of randomization compared to blinding is obvious – randomization can be implemented essentially at no cost, while blinding requires at least some investment of resources and may therefore have a negative impact on the research unit’s apparent capacity (measured by the number of completed studies, irrespective of quality).

Since the costs and resources are not an acceptable argument in discussions on ethical conduct of research, we often engage a defense mechanism, called rationalization, that helps to justify and explain why blinding should not be applied and do so in a seemingly rational or logical manner to avoid the true explanation. Arguments against the use of blinding can be divided into two groups.

One group comprises a range of factors that are essentially psychological barriers that can be effectively addressed. For example, one may believe that his/her research area or a specific research method has an innate immunity against any risk of bias. Or, alternatively, one may believe that his/her scientific excellence and the ability to supervise the activities in the lab make blinding unnecessary. There is a great example that can be used to illustrate that there is no place for beliefs and one should rather rely on empirical evidence. For decades, compared to male musicians, females have been underrepresented in major symphonic orchestras despite having equal access to high-quality education. The situation started to change in the mid-1970s when blind auditions were introduced and the proportion of female orchestrants went up (Goldin and Rouse 2000 ). In preclinical research, there are also examples of the impact of blinding (or a lack thereof). More specifically, there were studies that reveal substantially higher effect sizes reported in the experiments that were not randomized or blinded (Macleod et al. 2008 ).

Another potential barrier is related to the “trust” within the lab. Bench scientists need to be explained what the purpose of blinding is and, in the ideal case, be actively involved in development and implementation of blinding and other research rigor measures. With the proper explanation and engagement, blinding will not be seen as an unfriendly act whereby a PI or a lab head communicates a lack of trust.

The second group of arguments against the use of blinding is actually composed of legitimate questions that need to be addressed when designing an experiment. As mentioned above in the section on randomization, a decision to apply blinding should be justified by the needs of a specific experiment and correctly balanced against the existing and potential risks.

3.1 Fit-for-Purpose Blinding

It requires no explanation that, in preclinical research, there are no double-blinded studies in a sense of how it is meant in the clinic. However, similar to clinical research, blinding in preclinical experiments serves to protect against two potential sources of bias: bias related to blinding of personnel involved in study conduct including application of treatments (performance bias) and bias related to blinding of personnel involved in the outcome assessment (detection bias).

Analysis of the risks of bias in a particular research environment or for a specific experiment allows to decide which type of blinding should be applied and whether blinding is an appropriate measure against the risks.

There are three types or levels of blinding, and each one of them has its use: assumed blinding, partial blinding, and full blinding. With each type of blinding, experimenters allocate subjects to groups, replace the group names with blind codes, save the coding information in a secure place, and do not access this information until a certain pre-defined time point (e.g., until the data are collected or the study is completed and analyzed).

3.1.1 Assumed Blinding

In the assumed blinding, experimenters have access to the group or treatment codes at all times, but they do not know the correspondence between group and treatment before the end of the study. With the partial or full blinding, experimenters do not have access to the coding information until a certain pre-defined time point.

Main advantage of the assumed blinding is that an experiment can be conducted by one person who plans, performs, and analyzes the study. The risk of bias may be relatively low if the experiments are routine – e.g., lead optimization research in drug discovery or fee-for-service studies conducted using well-established standardized methods.

Efficiency of assumed blinding is enhanced if there is a sufficient time gap between application of a treatment and the outcome recording/assessment. It is also usually helpful if the access to the blinding codes is intentionally made more difficult (e.g., blinding codes are kept in the study design assistant or in a file on an office computer that is not too close to the lab where the outcomes will be recorded).

If introduced properly, assumed blinding can guard against certain unwanted practices such as remeasurement, removal, and reclassification of individual observations or data points (three evil Rs according to Shun-Shin and Francis 2013 ). In preclinical studies with small sample sizes, such practices have particularly deleterious consequences. In some cases, remeasurement even of a single subject may skew the results in a direction suggested by the knowledge of group allocation. One should emphasize that blinding is not necessarily an instrument against the remeasurement (it is often needed or unavoidable) but rather helps to avoid risks associated with it.

3.1.2 Partial Blinding

There are various situations where blinding (with no access to the blinding codes) is implemented not for the entire experiment but only for a certain part of it, e.g.:

No blinding during the application of experimental treatment (e.g., injection of a test drug) but proper blinding during the data collection and analysis

No blinding during the conduct of an experiment but proper blinding during analysis

For example, in behavioral pharmacology, there are experiments where subjects’ behavior is video recorded after a test drug is applied. In such cases, blinding is applied to analysis of the video recordings but not the drug application phase. Needless to say, blinded analysis has typically to be performed by someone who was not involved in the drug application phase.

A decision to apply partial blinding is based on (a) the confidence that the risks of bias are properly controlled during the unblinded parts of the experiment and/or (b) rationale assessment of the risks associated with maintaining blinding throughout the experiment. As an illustration of such decision-making process, one may imagine a study where the experiment is conducted in a small lab (two or three people) by adequately trained personnel that is not under pressure to deliver results of a certain pattern, data collection is automatic, and data integrity is maintained at every step. Supported by various risk reduction measures, such an experiment may deliver robust and reliable data even if not fully blinded.

Importantly, while partial blinding can adequately limit the risk of some forms of bias, it may be less effective against the performance bias.

3.1.3 Full Blinding

For important decision-enabling studies (including confirmatory research, see chapter “Resolving the Tension Between Exploration and Confirmation in Preclinical Biomedical Research”), it is usually preferable to implement full blinding rather than to explain why it was not done and argue that all the risks were properly controlled.

It is particularly advisable to follow full blinding in the experiments that are for some reasons difficult to repeat. For example, these could be studies running over significant periods of time (e.g., many months) or studies using unique resources or studies that may not be repeated for ethical reasons. In such cases, it is more rational to apply full blinding rather than leave a chance that the results will be questioned on the ground of lacking research rigor.

As implied by the name, full blinding requires complete allocation concealment from the beginning until the end of the experiment. This requirement may translate into substantial costs of resources. In the ideal scenario, each study should be supported by at least three independent people responsible for:

(De)coding, randomization

Conduct of the experiment such as handling of the subjects and application of test drugs (outcome recording and assessment)

(Outcome recording and assessment), final analysis

The main reason for separating conduct of the experiment and the final analysis is to protect against potential unintended unblinding (see below). If there is no risk of unblinding or it is not possible to have three independent people to support the blinding of an experiment, one may consider a single person responsible for every step from the conduct of the experiment to the final analysis. In other words, the study would be supported by two independent people responsible for:

Conduct of the experiment such as handling of the subjects and application of test drugs, outcome recording and assessment, and final analysis

3.2 Implementation of Blinding

Successful blinding is related to adequate randomization. This does not mean that they should always be performed in this sequence: first randomization and then blinding. In fact, the order may be reversed. For example, one may work with an offspring of the female rats that received experimental and control treatments while pregnant. As the litter size may differ substantially between the dams, randomization may be conducted after the pups are born, and this does not require allocation concealment to be broken.

The blinding procedure has to be carefully thought through. There are several factors that are listed below and that can turn a well-minded intention into a waste of resources.

First, blinding should as far as possible cover the entire experimental setup – i.e., all groups and subjects. There is an unacceptable practice to exclude positive controls from blinding that is often not justified by anything other than an intention to introduce a detection bias in order to reduce the risk of running an invalid experiment (i.e., an experiment where a positive control failed).

In some cases, positive controls cannot be administered by the same route or using the same pretreatment time as other groups. Typically, such a situation would require a separate negative (vehicle) control treated in the same way as the positive control group. Thus, the study is only partially blinded as the experimenter is able to identify the groups needed to “validate” the study (negative control and positive control groups) but remains blind to the exact nature of the treatment received by each of these two groups. For a better control over the risk of unblinding, one may apply a “double-dummy” approach where all animals receive the same number of administrations via the same routes and pretreatment times.

Second, experiments may be unintentionally unblinded. For example, drugs may have specific, easy to observe physicochemical characteristics, or drug treatments may change the appearance of the subjects or produce obvious adverse effects. Perhaps, even more common is the unblinding due to the differences in the appearance of the drug solution or suspension dependent on the concentration. In such cases, there is not much that can be done but it is essential to take corresponding notes and acknowledge in the study report or publication. It is interesting to note that the unblinding is often cited as an argument against the use of blinding (Fitzpatrick et al. 2018 ); however, this argument reveals another problem – partial blinding schemes are often applied as a normative response without any proper risk of bias assessment.

Third, blinding codes should be kept in a secure place avoiding any risk that the codes are lost. For in vivo experiments, this is an ethical requirement as the study will be wasted if it cannot be unblinded at the end.

Fourth, blinding can significantly increase the risk of mistakes. A particular situation that one should be prepared to avoid is related to lack of accessibility of blinding codes in case of emergency. There are situations when a scientist conducting a study falls ill and the treatment schedules or outcome assessment protocols are not available or a drug treatment is causing disturbing adverse effects and attending veterinarians or caregivers call for a decision in the absence of a scientist responsible for a study. It usually helps to make the right decision if it is known that an adverse effect is observed in a treatment group where it can be expected. Such situations should be foreseen and appropriate guidance made available to anyone directly or indirectly involved in an experiment. A proper study design should define a backup person with access to the blinding codes and include clear definition of endpoints.

Several practical tips can help to reduce the risk of human-made mistakes. For example, the study conduct can be greatly facilitated if each treatment group is assigned its own color. Then, this color coding would be applied to vials with the test drugs, syringes used to apply the drug, and the subjects (e.g., apply solution from a green-labeled vial using a green-labeled syringe to an animal from a green-labeled cage or with a green mark on its tail). When following such practice, one should not forget to randomly assign color codes to treatment conditions. Otherwise, for example, yellow color is always used for vehicle control, green for the lowest dose, and so forth.

To sum up, it is not always lacking resources that make full blinding not possible to apply. Further, similar to what was described above for randomization, there are clear exception cases where application of blinding is made problematic by the very nature of the experiment itself.

4 Concluding Recommendations

Most, if not all, guidelines, recommendations, and other texts on Good Research Practice emphasize the importance of blinding and randomization (chapters “Guidelines and Initiatives for Good Research Practice”, and “General Principles of Preclinical Study Design”). There is, however, very limited specific guidance on when and how to apply blinding and randomization. The present chapter aims to close this gap.

Generally speaking, experiments should be blinded and randomized if:

This is a confirmatory research (see chapter “Resolving the Tension Between Exploration and Confirmation in Preclinical Biomedical Research”) that has a major impact on decision-making and that cannot be readily repeated (for ethical or resource-related reasons).

No other measures can be applied to protect against existing and potential risks of bias.

There are various sources of bias that affect the outcome of experimental studies and these sources are unique and specific to each research unit. There is usually no one who knows these risks better than the scientists working in the research unit, and it is always up to the scientist to decide if, when, and how blinding and randomization should be implemented. However, there are several recommendations that can help to decide and act in the most effective way:

Conduct a risk assessment for your research environment, and, if you do not know how to do that, ask for a professional support or advice.

Involve your team in developing and implementing the blinding/randomization protocols, and seek the team members’ feedback regarding the performance of these protocols (and revise them, as needed).

Provide training not only on how to administer blinding and randomization but also to preempt any questions related to the rationale behind these measures (i.e., experiments are blinded not because of the suspected misconduct or lack of trust).

Describe blinding and randomization procedures in dedicated protocols with as many details as possible (including emergency plans and accident reporting, as discussed above).

Ensure maximal transparency when reporting blinding and randomization (e.g., in a publication). When deciding to apply blinding and randomization, be maximally clear about the details (Table 4 ). When deciding against, be open about the reasons for such decision. Transparency is also essential when conducting multi-laboratory collaborative projects or when a study is outsourced to another laboratory. To avoid any misunderstanding, collaborators should specify expectations and reach alignment on study design prior to the experiment and communicate all important details in study reports.

Blinding and randomization should always be a part of a more general effort to introduce and maintain research rigor. Just as the randomization increases the likelihood that blinding will not be omitted (van der Worp et al. 2010 ), other Good Research Practices such as proper documentation are also highly instrumental in making blinding and randomization effective.

To conclude, blinding and randomization may be associated with some effort and additional costs, but, under all circumstances, a decision to apply these research rigor techniques should not be based on general statements and arguments by those who do not want to leave their comfort zone. Instead, the decision should be based on the applicable risk assessment and careful review of potential implementation burden. In many cases, this leads to a relieving discovery that the devil is not so black as he is painted.

https://stats.stackexchange.com/questions/74350/is-randomization-reliable-with-small-samples .

Carroll L (1871) Through the looking-glass, and what Alice found there. ICU Publishing

Google Scholar  

Fitzpatrick BG, Koustova E, Wang Y (2018) Getting personal with the “reproducibility crisis”: interviews in the animal research community. Lab Anim 47:175–177

Article   Google Scholar  

Goldin C, Rouse C (2000) Orchestrating impartiality: the impact of “blind” auditions on female musicians. Am Econ Rev 90:715–741

Hooijmans CR, Rovers MM, de Vries RB, Leenaars M, Ritskes-Hoitinga M, Langendam MW (2014) SYRCLE’s risk of bias tool for animal studies. BMC Med Res Methodol 14:43

ICH Harmonised Tripartite Guideline (1998) Statistical principles for clinical trials (E9). CPMP/ICH/363/96, March 1998

Jones B, Kenward MG (2003) Design and analysis of cross-over designs, 2nd edn. Chapman and Hall, London

Kalish LA, Begg GB (1985) Treatment allocation methods in clinical trials a review. Stat Med 4:129–144

Article   CAS   Google Scholar  

Krauth D, Woodruff TJ, Bero L (2013) Instruments for assessing risk of bias and other methodological criteria of published animal studies: a systematic review. Environ Health Perspect 121:985–992

Macleod MR, The NPQIP Collaborative Group (2017) Findings of a retrospective, controlled cohort study of the impact of a change in Nature journals’ editorial policy for life sciences research on the completeness of reporting study design and execution. bioRxiv:187245. https://doi.org/10.1101/187245

Macleod MR, van der Worp HB, Sena ES, Howells DW, Dirnagl U, Donnan GA (2008) Evidence for the efficacy of NXY-059 in experimental focal cerebral ischaemia is confounded by study quality. Stroke 39:2824–2829

Shun-Shin MJ, Francis DP (2013) Why even more clinical research studies may be false: effect of asymmetrical handling of clinically unexpected values. PLoS One 8(6):e65323

van der Worp HB, Howells DW, Sena ES, Porritt MJ, Rewell S, O’Collins V, Macleod MR (2010) Can animal models of disease reliably inform human studies? PLoS Med 7(3):e1000245

Download references

Acknowledgments

The authors would like to thank Dr. Thomas Steckler (Janssen), Dr. Kim Wever (Radboud University), and Dr. Jan Vollert (Imperial College London) for reading the earlier version of the manuscript and providing comments and suggestions.

Author information

Authors and affiliations.

Partnership for Assessment and Accreditation of Scientific Practice, Heidelberg, Germany

Anton Bespalov

Pavlov Medical University, St. Petersburg, Russia

AbbVie, Ludwigshafen, Germany

Karsten Wicke

Porsolt, Le Genest-Saint-Isle, France

Vincent Castagné

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Anton Bespalov .

Editor information

Editors and affiliations.

Partnership for Assessment & Accreditation of Scientific Practice, Heidelberg, Baden-Württemberg, Germany

Department of Pharmacology, Johannes Gutenberg University, Mainz, Rheinland-Pfalz, Germany

Martin C. Michel

Janssen Pharmaceutica N.V., Beerse, Belgium

Thomas Steckler

Rights and permissions

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and permissions

Copyright information

© 2019 The Author(s)

About this chapter

Cite this chapter.

Bespalov, A., Wicke, K., Castagné, V. (2019). Blinding and Randomization. In: Bespalov, A., Michel, M., Steckler, T. (eds) Good Research Practice in Non-Clinical Pharmacology and Biomedicine. Handbook of Experimental Pharmacology, vol 257. Springer, Cham. https://doi.org/10.1007/164_2019_279

Download citation

DOI : https://doi.org/10.1007/164_2019_279

Published : 07 November 2019

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-33655-4

Online ISBN : 978-3-030-33656-1

eBook Packages : Biomedical and Life Sciences Biomedical and Life Sciences (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Publish with us

Policies and ethics

  • Find a journal
  • Track your research

HIGH SCHOOL

  • ACT Tutoring
  • SAT Tutoring
  • PSAT Tutoring
  • ASPIRE Tutoring
  • SHSAT Tutoring
  • STAAR Tutoring

GRADUATE SCHOOL

  • MCAT Tutoring
  • GRE Tutoring
  • LSAT Tutoring
  • GMAT Tutoring
  • AIMS Tutoring
  • HSPT Tutoring
  • ISAT Tutoring
  • SSAT Tutoring

Search 50+ Tests

Loading Page

math tutoring

  • Elementary Math
  • Pre-Calculus
  • Trigonometry

science tutoring

Foreign languages.

  • Mandarin Chinese

elementary tutoring

  • Computer Science

Search 350+ Subjects

  • Video Overview
  • Tutor Selection Process
  • Online Tutoring
  • Mobile Tutoring
  • Instant Tutoring
  • How We Operate
  • Our Guarantee
  • Impact of Tutoring
  • Reviews & Testimonials
  • Media Coverage
  • About Varsity Tutors

AP Statistics : How to do random assignments in an experiment

Study concepts, example questions & explanations for ap statistics, all ap statistics resources, example questions, example question #1 : data collection.

A researcher wants to randomly assign participants to a treatment and control group. Which of the following approaches ensures that the treatment assignment is random?

Assigning the treatment based on who needs it the most 

Obtaining nationally representative samples for both

Assigning the treatment by gender

Flipping a coin

The only random procedure here is the coin flip. In expectation, the coin flip ensures that no background variables influence treatment assignment whereas the other examples either have nothing to do with random assignment (e.g. nationally representative sample) or completely contradict the purpose of random assignment (e.g. assigning the treatment based on who needs it the most). 

Display vt optimized

Report an issue with this question

If you've found an issue with this question, please let us know. With the help of the community we can continue to improve our educational resources.

DMCA Complaint

If you believe that content available by means of the Website (as defined in our Terms of Service) infringes one or more of your copyrights, please notify us by providing a written notice (“Infringement Notice”) containing the information described below to the designated agent listed below. If Varsity Tutors takes action in response to an Infringement Notice, it will make a good faith attempt to contact the party that made such content available by means of the most recent email address, if any, provided by such party to Varsity Tutors.

Your Infringement Notice may be forwarded to the party that made the content available or to third parties such as ChillingEffects.org.

Please be advised that you will be liable for damages (including costs and attorneys’ fees) if you materially misrepresent that a product or activity is infringing your copyrights. Thus, if you are not sure content located on or linked-to by the Website infringes your copyright, you should consider first contacting an attorney.

Please follow these steps to file a notice:

You must include the following:

A physical or electronic signature of the copyright owner or a person authorized to act on their behalf; An identification of the copyright claimed to have been infringed; A description of the nature and exact location of the content that you claim to infringe your copyright, in \ sufficient detail to permit Varsity Tutors to find and positively identify that content; for example we require a link to the specific question (not just the name of the question) that contains the content and a description of which specific portion of the question – an image, a link, the text, etc – your complaint refers to; Your name, address, telephone number and email address; and A statement by you: (a) that you believe in good faith that the use of the content that you claim to infringe your copyright is not authorized by law, or by the copyright owner or such owner’s agent; (b) that all of the information contained in your Infringement Notice is accurate, and (c) under penalty of perjury, that you are either the copyright owner or a person authorized to act on their behalf.

Send your complaint to our designated agent at:

Charles Cohn Varsity Tutors LLC 101 S. Hanley Rd, Suite 300 St. Louis, MO 63105

Or fill out the form below:

Contact Information

Complaint details.

Learning Tools by Varsity Tutors

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings
  • Advanced Search
  • Journal List
  • Int J Environ Res Public Health

Logo of ijerph

Blocked Randomization with Randomly Selected Block Sizes

When planning a randomized clinical trial, careful consideration must be given to how participants are selected for various arms of a study. Selection and accidental bias may occur when participants are not assigned to study groups with equal probability. A simple random allocation scheme is a process by which each participant has equal likelihood of being assigned to treatment versus referent groups. However, by chance an unequal number of individuals may be assigned to each arm of the study and thus decrease the power to detect statistically significant differences between groups. Block randomization is a commonly used technique in clinical trial design to reduce bias and achieve balance in the allocation of participants to treatment arms, especially when the sample size is small. This method increases the probability that each arm will contain an equal number of individuals by sequencing participant assignments by block. Yet still, the allocation process may be predictable, for example, when the investigator is not blind and the block size is fixed. This paper provides an overview of blocked randomization and illustrates how to avoid selection bias by using random block sizes.

1. Introduction

The purpose of randomization is to achieve balance with respect to known and unknown risk factors in the allocation of participants to treatment arms in a study [ 1 , 2 ]. A premise of basic statistical tests of significance is that underlying observations are independently and identically distributed. The stochastic assignment of participants helps to satisfy this requirement. It also allows the investigator to determine whether observed differences between groups are due to the agent being studied or chance.

By probability, a simple randomization scheme may allocate a different number of participants to each study group. This may reduce the power of a statistical procedure to reject the null hypothesis as statistical power is maximized for equal sample sizes [ 3 ]. Additionally, an imbalance of treatment groups within confounding factors may occur. This is especially true for small sample sizes. Confounding distorts the statistical validity of statistical inferences about cause and effect. The failure to control for confounding may inflate type 1 error and erroneously lead to the conclusion that a putative risk factor is causally associated with the outcome under study ( i.e. , false positive finding). A chance run of participants to a particular study group also may occur under a simple randomization scenario. This can lead to bias, for example, if the initial participants in the trial are healthier than the later ones [ 1 ]. Blocked randomization offers a simple means to achieve balance between study arms and to reduce the opportunity for bias and confounding.

2. Methodology

Block randomization works by randomizing participants within blocks such that an equal number are assigned to each treatment. For example, given a block size of 4, there are 6 possible ways to equally assign participants to a block. Allocation proceeds by randomly selecting one of the orderings and assigning the next block of participants to study groups according to the specified sequence. Note that repeat blocks may occur when the total sample size is greater than the block size times the number of possible orderings. Furthermore, the block size must be divisible by the number of study groups.

A disadvantage of block randomization is that the allocation of participants may be predictable and result in selection bias when the study groups are unmasked. That is, the treatment assignment that has so far occurred least often in the block likely will be the next chosen [ 4 ]. Selection bias may be reduced by using random block sizes and keeping the investigator blind to the size of each block.

2.1. Example

An investigator wishes to compare a family-based educational intervention for childhood weight loss with a standard individual-base program. A planned enrollment of 250 participants, 50 per study site, is to be randomly assigned to the two intervention arms. Below, a computer algorithm written in SAS ® (Cary, NC) is presented for performing a block randomization with randomly selected block sizes of 4, 8 and 12 ( Figure 1 ). The macro generates 15 randomized block allocations each for 5 study sites. A greater number of blocks are created than is necessary in the event that the investigator continues enrollment beyond the initially planned sample size. For example, expanded enrollment might occur due to a greater than anticipated attrition rate.

An external file that holds a picture, illustration, etc.
Object name is ijerph-08-00015f1.jpg

SAS algorithm to perform blocked randomization with random block sizes.

The macro works by invoking the ranuni function to equally partition the number of blocks according to a uniform distribution. When the number within the parenthesis of the ranuni function equals zero the seed is determined by the computer system clock. Thus, a different set of block allocations occur each time the macro is executed. Changing the number to a positive integer will assure that the same block allocation is generated during subsequent use of the macro. After the block size is randomly determined the macro efficiently allocates treatment assignment equally within blocks by sorting on the looping index variable. Although the macro only generates 3 randomly selected block sizes the code may be easily modified to increase this number by further partitioning the uniform assignment space. Similarly, the number of study sites and blocks may be increased or decreased by changing the upper range of the two program do-loops. The output of the SAS algorithm corresponding to the first 3 blocks for Site 1 is shown in Figure 2 . For example, Block = 1 randomizes 4 participants, with the first two assigned to “Non-intervention” and the last two assigned to “Intervention”.

An external file that holds a picture, illustration, etc.
Object name is ijerph-08-00015f2.jpg

Example output from the SAS algorithm.

3. Discussion

A key advantage of blocked randomization is that treatment groups will be equal in size and will tend to be uniformly distributed by key outcome-related characteristics. Typically, smaller block sizes will lead to more balanced groups by time than larger block sizes. However, a small block size increases the risk that the allocation process may be predictable, especially if the assignment is open or there is a chance for unmasking of the treatment assignment. For example, certain immunosuppressive agents change color when exposed to light. This may inadvertently expose the identity of the compound in a clinical trial if the comparator compound is not light sensitive. Unmasking also may be intentional in the case of a physician chemically analyzing a patient’s blood to determine the identity of the randomized drug.

Using a large block size will help protect against the investigator predicting the treatment sequence. However, if one treatment occurs with greater frequency at the beginning of a block, a mid-block inequality can occur if there is an interim analysis or the study is terminated midway through a block. Alternatively, keeping block sizes small and using random sequences of block sizes can ameliorate this problem. Another option is to use larger random block sizes but offset the chance of initial treatment runs within a block by allocating participants using a biased coin approach [ 4 ]. In a simple trial consisting of a single treatment and referent group, this method probabilistically assigns participants within a block to the treatment arm depending on the assignment balance of participants thus far randomized to the treatment arm. For example, if a participant to be randomized is in a category which has K more treatments (t) than referents (r) already assigned, then assignment to the treatment and referent group will be made with probability t = q, (r = p), t = ½ (r = ½), and t = p, (r = q) contingent on whether K is greater than, equal to, or less than zero (where p ≥ q, p + q = 1). Although the latter strategy may distort the randomization process by decreasing the probability of long runs, the resulting bias may be acceptable if it prevents mid-block inequality and controls the predictability of treatment assignment. Under certain minimax conditions, the random coin approach has been shown to be superior to complete randomization for minimizing accidental bias (e.g., a type of bias that occurs when the randomization scheme does not achieve balance on outcome-related covariates) [ 4 ]. A key advantage of the open source algorithm provided in this paper, and comparable algorithms available in programming languages such as R [ 5 ], is that the underlying code may be modified to accommodate the random coin technique and other balancing strategies yet to be implemented in standard statistical packages.

The number of participants assigned to each treatment group will be equal when all the blocks are the same size and the overall study sample size is a multiple of the block size. Furthermore, in the case of unequal block sizes, balance is guaranteed if all treatment assignments are made within the final block [ 1 ]. However, when random block sizes are used in a multi-site study, the sample size may vary by site but on average will be similar.

The advantage of using random block sizes to reduce selection bias is only observed when assignments can be determined with certainty [ 1 ]. That is, when the assignment is not known with certainty but rather is just more probable, then there is no advantage to using random block sizes. The best protection against selection bias is to blind both the ordering of blocks and their respective size. Furthermore, the use of random block sizes is not necessary in an unmasked trial if participants have been randomized as a block rather than individually according to their entry into the study, as the former will completely eliminate selection bias.

The necessity to take into account blocking in the statistical analysis of the data, including when the block sizes are randomly chosen, depends on whether an intrablock correlation exists [ 1 ]. A non-zero intrablock correlation may occur, for example, when the characteristics and responses for a participant change according to their entry time into the study. If the process is homogeneous the intrablock correlation will equal zero and blocking may be ignored in the analysis. However, variance estimates must be appropriately adjusted when intrablock correlation is present [ 6 ]. The presence of missing data within blocks also can potentially complicate the validity of statistical analysis. For example, special analytic techniques may be needed when the missing data is related to treatment effects or occurs in some other non random manner [ 1 , 3 ]. However, datasets with missing-at-random observations may be analyzed by simply excluding the affected blocks. When possible, measures should be implemented to minimize missing values as their presence will reduce the power of statistical procedures.

Significant treatment imbalances and accidental bias typically do not occur in large blinded trials, especially if randomization can be performed at the onset of the study. However, when treatment assignment is open and sample size is small than a block randomization procedure with randomly chosen block sizes may help maintain balance of treatment assignment and reduce the potential for selection bias.

Acknowledgements

The author thanks Katherine T. Jones for valuable comments during the writing of this manuscript and her knowledge and insight are greatly appreciated. The contents of this publication are solely the responsibility of the author and do not necessarily represent the views of any institution or funding agency.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

  • Knowledge Base
  • Methodology
  • Random Assignment in Experiments | Introduction & Examples

Random Assignment in Experiments | Introduction & Examples

Published on 6 May 2022 by Pritha Bhandari . Revised on 13 February 2023.

In experimental research, random assignment is a way of placing participants from your sample into different treatment groups using randomisation.

With simple random assignment, every member of the sample has a known or equal chance of being placed in a control group or an experimental group. Studies that use simple random assignment are also called completely randomised designs .

Random assignment is a key part of experimental design . It helps you ensure that all groups are comparable at the start of a study: any differences between them are due to random factors.

Table of contents

Why does random assignment matter, random sampling vs random assignment, how do you use random assignment, when is random assignment not used, frequently asked questions about random assignment.

Random assignment is an important part of control in experimental research, because it helps strengthen the internal validity of an experiment.

In experiments, researchers manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables. To do so, they often use different levels of an independent variable for different groups of participants.

This is called a between-groups or independent measures design.

You use three groups of participants that are each given a different level of the independent variable:

  • A control group that’s given a placebo (no dosage)
  • An experimental group that’s given a low dosage
  • A second experimental group that’s given a high dosage

Random assignment to helps you make sure that the treatment groups don’t differ in systematic or biased ways at the start of the experiment.

If you don’t use random assignment, you may not be able to rule out alternative explanations for your results.

  • Participants recruited from pubs are placed in the control group
  • Participants recruited from local community centres are placed in the low-dosage experimental group
  • Participants recruited from gyms are placed in the high-dosage group

With this type of assignment, it’s hard to tell whether the participant characteristics are the same across all groups at the start of the study. Gym users may tend to engage in more healthy behaviours than people who frequent pubs or community centres, and this would introduce a healthy user bias in your study.

Although random assignment helps even out baseline differences between groups, it doesn’t always make them completely equivalent. There may still be extraneous variables that differ between groups, and there will always be some group differences that arise from chance.

Most of the time, the random variation between groups is low, and, therefore, it’s acceptable for further analysis. This is especially true when you have a large sample. In general, you should always use random assignment in experiments when it is ethically possible and makes sense for your study topic.

Prevent plagiarism, run a free check.

Random sampling and random assignment are both important concepts in research, but it’s important to understand the difference between them.

Random sampling (also called probability sampling or random selection) is a way of selecting members of a population to be included in your study. In contrast, random assignment is a way of sorting the sample participants into control and experimental groups.

While random sampling is used in many types of studies, random assignment is only used in between-subjects experimental designs.

Some studies use both random sampling and random assignment, while others use only one or the other.

Random sample vs random assignment

Random sampling enhances the external validity or generalisability of your results, because it helps to ensure that your sample is unbiased and representative of the whole population. This allows you to make stronger statistical inferences .

You use a simple random sample to collect data. Because you have access to the whole population (all employees), you can assign all 8,000 employees a number and use a random number generator to select 300 employees. These 300 employees are your full sample.

Random assignment enhances the internal validity of the study, because it ensures that there are no systematic differences between the participants in each group. This helps you conclude that the outcomes can be attributed to the independent variable .

  • A control group that receives no intervention
  • An experimental group that has a remote team-building intervention every week for a month

You use random assignment to place participants into the control or experimental group. To do so, you take your list of participants and assign each participant a number. Again, you use a random number generator to place each participant in one of the two groups.

To use simple random assignment, you start by giving every member of the sample a unique number. Then, you can use computer programs or manual methods to randomly assign each participant to a group.

  • Random number generator: Use a computer program to generate random numbers from the list for each group.
  • Lottery method: Place all numbers individually into a hat or a bucket, and draw numbers at random for each group.
  • Flip a coin: When you only have two groups, for each number on the list, flip a coin to decide if they’ll be in the control or the experimental group.
  • Use a dice: When you have three groups, for each number on the list, roll a die to decide which of the groups they will be in. For example, assume that rolling 1 or 2 lands them in a control group; 3 or 4 in an experimental group; and 5 or 6 in a second control or experimental group.

This type of random assignment is the most powerful method of placing participants in conditions, because each individual has an equal chance of being placed in any one of your treatment groups.

Random assignment in block designs

In more complicated experimental designs, random assignment is only used after participants are grouped into blocks based on some characteristic (e.g., test score or demographic variable). These groupings mean that you need a larger sample to achieve high statistical power .

For example, a randomised block design involves placing participants into blocks based on a shared characteristic (e.g., college students vs graduates), and then using random assignment within each block to assign participants to every treatment condition. This helps you assess whether the characteristic affects the outcomes of your treatment.

In an experimental matched design , you use blocking and then match up individual participants from each block based on specific characteristics. Within each matched pair or group, you randomly assign each participant to one of the conditions in the experiment and compare their outcomes.

Sometimes, it’s not relevant or ethical to use simple random assignment, so groups are assigned in a different way.

When comparing different groups

Sometimes, differences between participants are the main focus of a study, for example, when comparing children and adults or people with and without health conditions. Participants are not randomly assigned to different groups, but instead assigned based on their characteristics.

In this type of study, the characteristic of interest (e.g., gender) is an independent variable, and the groups differ based on the different levels (e.g., men, women). All participants are tested the same way, and then their group-level outcomes are compared.

When it’s not ethically permissible

When studying unhealthy or dangerous behaviours, it’s not possible to use random assignment. For example, if you’re studying heavy drinkers and social drinkers, it’s unethical to randomly assign participants to one of the two groups and ask them to drink large amounts of alcohol for your experiment.

When you can’t assign participants to groups, you can also conduct a quasi-experimental study . In a quasi-experiment, you study the outcomes of pre-existing groups who receive treatments that you may not have any control over (e.g., heavy drinkers and social drinkers).

These groups aren’t randomly assigned, but may be considered comparable when some other variables (e.g., age or socioeconomic status) are controlled for.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bhandari, P. (2023, February 13). Random Assignment in Experiments | Introduction & Examples. Scribbr. Retrieved 4 March 2024, from https://www.scribbr.co.uk/research-methods/random-assignment-experiments/

Is this article helpful?

Pritha Bhandari

Pritha Bhandari

Other students also liked, a quick guide to experimental design | 5 steps & examples, controlled experiments | methods & examples of control, control groups and treatment groups | uses & examples.

Random Assignment in Psychology: Definition & Examples

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

In psychology, random assignment refers to the practice of allocating participants to different experimental groups in a study in a completely unbiased way, ensuring each participant has an equal chance of being assigned to any group.

In experimental research, random assignment, or random placement, organizes participants from your sample into different groups using randomization. 

Random assignment uses chance procedures to ensure that each participant has an equal opportunity of being assigned to either a control or experimental group.

The control group does not receive the treatment in question, whereas the experimental group does receive the treatment.

When using random assignment, neither the researcher nor the participant can choose the group to which the participant is assigned. This ensures that any differences between and within the groups are not systematic at the onset of the study. 

In a study to test the success of a weight-loss program, investigators randomly assigned a pool of participants to one of two groups.

Group A participants participated in the weight-loss program for 10 weeks and took a class where they learned about the benefits of healthy eating and exercise.

Group B participants read a 200-page book that explains the benefits of weight loss. The investigator randomly assigned participants to one of the two groups.

The researchers found that those who participated in the program and took the class were more likely to lose weight than those in the other group that received only the book.

Importance 

Random assignment ensures that each group in the experiment is identical before applying the independent variable.

In experiments , researchers will manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables. Random assignment increases the likelihood that the treatment groups are the same at the onset of a study.

Thus, any changes that result from the independent variable can be assumed to be a result of the treatment of interest. This is particularly important for eliminating sources of bias and strengthening the internal validity of an experiment.

Random assignment is the best method for inferring a causal relationship between a treatment and an outcome.

Random Selection vs. Random Assignment 

Random selection (also called probability sampling or random sampling) is a way of randomly selecting members of a population to be included in your study.

On the other hand, random assignment is a way of sorting the sample participants into control and treatment groups. 

Random selection ensures that everyone in the population has an equal chance of being selected for the study. Once the pool of participants has been chosen, experimenters use random assignment to assign participants into groups. 

Random assignment is only used in between-subjects experimental designs, while random selection can be used in a variety of study designs.

Random Assignment vs Random Sampling

Random sampling refers to selecting participants from a population so that each individual has an equal chance of being chosen. This method enhances the representativeness of the sample.

Random assignment, on the other hand, is used in experimental designs once participants are selected. It involves allocating these participants to different experimental groups or conditions randomly.

This helps ensure that any differences in results across groups are due to manipulating the independent variable, not preexisting differences among participants.

When to Use Random Assignment

Random assignment is used in experiments with a between-groups or independent measures design.

In these research designs, researchers will manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables.

There is usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable at the onset of the study.

How to Use Random Assignment

There are a variety of ways to assign participants into study groups randomly. Here are a handful of popular methods: 

  • Random Number Generator : Give each member of the sample a unique number; use a computer program to randomly generate a number from the list for each group.
  • Lottery : Give each member of the sample a unique number. Place all numbers in a hat or bucket and draw numbers at random for each group.
  • Flipping a Coin : Flip a coin for each participant to decide if they will be in the control group or experimental group (this method can only be used when you have just two groups) 
  • Roll a Die : For each number on the list, roll a dice to decide which of the groups they will be in. For example, assume that rolling 1, 2, or 3 places them in a control group and rolling 3, 4, 5 lands them in an experimental group.

When is Random Assignment not used?

  • When it is not ethically permissible: Randomization is only ethical if the researcher has no evidence that one treatment is superior to the other or that one treatment might have harmful side effects. 
  • When answering non-causal questions : If the researcher is just interested in predicting the probability of an event, the causal relationship between the variables is not important and observational designs would be more suitable than random assignment. 
  • When studying the effect of variables that cannot be manipulated: Some risk factors cannot be manipulated and so it would not make any sense to study them in a randomized trial. For example, we cannot randomly assign participants into categories based on age, gender, or genetic factors.

Drawbacks of Random Assignment

While randomization assures an unbiased assignment of participants to groups, it does not guarantee the equality of these groups. There could still be extraneous variables that differ between groups or group differences that arise from chance. Additionally, there is still an element of luck with random assignments.

Thus, researchers can not produce perfectly equal groups for each specific study. Differences between the treatment group and control group might still exist, and the results of a randomized trial may sometimes be wrong, but this is absolutely okay.

Scientific evidence is a long and continuous process, and the groups will tend to be equal in the long run when data is aggregated in a meta-analysis.

Additionally, external validity (i.e., the extent to which the researcher can use the results of the study to generalize to the larger population) is compromised with random assignment.

Random assignment is challenging to implement outside of controlled laboratory conditions and might not represent what would happen in the real world at the population level. 

Random assignment can also be more costly than simple observational studies, where an investigator is just observing events without intervening with the population.

Randomization also can be time-consuming and challenging, especially when participants refuse to receive the assigned treatment or do not adhere to recommendations. 

What is the difference between random sampling and random assignment?

Random sampling refers to randomly selecting a sample of participants from a population. Random assignment refers to randomly assigning participants to treatment groups from the selected sample.

Does random assignment increase internal validity?

Yes, random assignment ensures that there are no systematic differences between the participants in each group, enhancing the study’s internal validity .

Does random assignment reduce sampling error?

Yes, with random assignment, participants have an equal chance of being assigned to either a control group or an experimental group, resulting in a sample that is, in theory, representative of the population.

Random assignment does not completely eliminate sampling error because a sample only approximates the population from which it is drawn. However, random sampling is a way to minimize sampling errors. 

When is random assignment not possible?

Random assignment is not possible when the experimenters cannot control the treatment or independent variable.

For example, if you want to compare how men and women perform on a test, you cannot randomly assign subjects to these groups.

Participants are not randomly assigned to different groups in this study, but instead assigned based on their characteristics.

Does random assignment eliminate confounding variables?

Yes, random assignment eliminates the influence of any confounding variables on the treatment because it distributes them at random among the study groups. Randomization invalidates any relationship between a confounding variable and the treatment.

Why is random assignment of participants to treatment conditions in an experiment used?

Random assignment is used to ensure that all groups are comparable at the start of a study. This allows researchers to conclude that the outcomes of the study can be attributed to the intervention at hand and to rule out alternative explanations for study results.

Further Reading

  • Bogomolnaia, A., & Moulin, H. (2001). A new solution to the random assignment problem .  Journal of Economic theory ,  100 (2), 295-328.
  • Krause, M. S., & Howard, K. I. (2003). What random assignment does and does not do .  Journal of Clinical Psychology ,  59 (7), 751-766.

Print Friendly, PDF & Email

Library homepage

  • school Campus Bookshelves
  • menu_book Bookshelves
  • perm_media Learning Objects
  • login Login
  • how_to_reg Request Instructor Account
  • hub Instructor Commons
  • Download Page (PDF)
  • Download Full Book (PDF)
  • Periodic Table
  • Physics Constants
  • Scientific Calculator
  • Reference & Cite
  • Tools expand_more
  • Readability

selected template will load here

This action is not available.

Biology LibreTexts

7.6: Genetic Variation

  • Last updated
  • Save as PDF
  • Page ID 16758

  • Suzanne Wakim & Mandeep Grewal
  • Butte College

What helps ensure the survival of a species?

Genetic variation. It is this variation that is the essence of evolution. Without genetic differences among individuals, "survival of the fittest" would not be likely. Either all survive, or all perish.

Profile pictures of diverse individuals

Genetic Variation

Sexual reproduction results in infinite possibilities of genetic variation. In other words, sexual reproduction results in offspring that are genetically unique. They differ from both parents and also from each other. This occurs for a number of reasons.

  • When homologous chromosomes form pairs during prophase I of meiosis I, crossing-over can occur. Crossing-over is the exchange of genetic material between homologous chromosomes. It results in new combinations of genes on each chromosome.
  • When cells divide during meiosis, homologous chromosomes are randomly distributed to daughter cells, and different chromosomes segregate independently of each other. This called is called independent assortment . It results in gametes that have unique combinations of chromosomes.
  • In sexual reproduction, two gametes unite to produce an offspring. But which two of the millions of possible gametes will it be? This is likely to be a matter of chance. It is obviously another source of genetic variation in offspring. This is known as random fertilization.

All of these mechanisms working together result in an amazing amount of potential variation. Each human couple, for example, has the potential to produce more than 64 trillion genetically unique children. No wonder we are all different!

Crossing-Over

Crossing-over occurs during prophase I, and it is the exchange of genetic material between non-sister chromatids of homologous chromosomes. Recall during prophase I, homologous chromosomes line up in pairs, gene-for-gene down their entire length, forming a configuration with four chromatids, known as a tetrad . At this point, the chromatids are very close to each other and some material from two chromatids switch chromosomes, that is, the material breaks off and reattaches at the same position on the homologous chromosome (Figure \(\PageIndex{2}\)). This exchange of genetic material can happen many times within the same pair of homologous chromosomes, creating unique combinations of genes. This process is also known as recombination .

During prophase I, chromosomes condense and become visible inside the nucleus. As the nuclear envelope begins to break down, homologous chromosomes move closer together. The synaptonemal complex, a lattice of proteins between the homologous chromosomes, forms at specific locations, spreading to cover the entire length of the chromosomes. The tight pairing of the homologous chromosomes is called synapsis. In synapsis, the genes on the chromatids of the homologous chromosomes are aligned with each other. The synaptonemal complex also supports the exchange of chromosomal segments between non-sister homologous chromatids in a process called crossing over. The crossover events are the first source of genetic variation produced by meiosis. A single crossover event between homologous non-sister chromatids leads to an exchange of DNA between chromosomes. Following crossover, the synaptonemal complex breaks down and the cohesin connection between homologous pairs is also removed. At the end of prophase I, the pairs are held together only at the chiasmata; they are called tetrads because the four sister chromatids of each pair of homologous chromosomes are now visible.

non sister chromatids of tetrad exchanged their genetic material

Independent Assortment and Random Fertilization

During metaphase I, the tetrads move to the metaphase plate with kinetochores facing opposite poles. The homologous pairs orient themselves randomly at the equator. This event is the second mechanism that introduces variation into the gametes or spores. In each cell that undergoes meiosis, the arrangement of the tetrads is different. The number of variations is dependent on the number of chromosomes making up a set. There are two possibilities for orientation at the metaphase plate. The possible number of alignments, therefore, equals 2 n , where n is the number of chromosomes per set. Given these two mechanisms, it is highly unlikely that any two haploid cells resulting from meiosis will have the same genetic composition.

independent assortment in two different way to give rise to variety of gametes.

In humans, there are over 8 million configurations in which the chromosomes can line up during metaphase I of meiosis. It is the specific process of meiosis, resulting in four unique haploid cells, that results in these many combinations. This independent assortment, in which the chromosome inherited from either the father or mother can sort into any gamete, produces the potential for tremendous genetic variation. Together with random fertilization, more possibilities for genetic variation exist between any two people than the number of individuals alive today. Sexual reproduction is the random fertilization of a gamete from the female using a gamete from the male. A sperm cell, with over 8 million chromosome combinations, fertilizes an egg cell, which also has over 8 million chromosome combinations. That is over 64 trillion unique combinations, not counting the unique combinations produced by crossing-over.

  • What is crossing-over and when does it occur?
  • Describe how crossing-over, independent assortment, and random fertilization lead to genetic variation.
  • How many combinations of chromosomes are possible from sexual reproduction in humans?
  • Create a diagram to show how crossing-over occurs and how it creates new gene combinations on each chromosome.

Explore More

Attributions.

  • Supplier Diversity Strategies by Profiles in Diversity Journal, CC BY 3.0 via Wikimedia Commons
  • Chromosomal Recombination by David Eccles ( Gringer ), licensed CC BY 2.5 via Wikimedia Commons
  • Crossing over by OpenStax, licensed CC BY 4.0
  • Independent assortment by OpenStax, licensed CC BY 4.0
  • Text adapted from Human Biology by CK-12 licensed CC BY-NC 3.0

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

  • We're Hiring!
  • Help Center

paper cover thumbnail

Random walk models in biology

Profile image of Edward Codling

2008, Journal of the …

Related Papers

Charo Osta Pinzolas

random assignment biology

Mathematical Modelling of Natural Phenomena

Sergei Fedotov

Journal of Mathematical Biology

Hans Othmer

Multidisciplinary Cancer Investigation

fateme safaeifard

Computing and Visualization in Science

Nicolae Suciu

Methodology and Computing in Applied Probability

Sylvie Roelly

Physical biology

Laurence Bugeon

While the majority of cells in an organism are static and remain relatively immobile in their tissue, migrating cells occur commonly during developmental processes and are crucial for a functioning immune response. The mode of migration has been described in terms of various types of random walks. To understand the details of the migratory behaviour we rely on mathematical models and their calibration to experimental data. Here we propose an approximate Bayesian inference scheme to calibrate a class of random walk models characterized by a specific, parametric particle re-orientation mechanism to observed trajectory data. We elaborate the concept of transition matrices (TMs) to detect random walk patterns and determine a statistic to quantify these TM to make them applicable for inference schemes. We apply the developed pipeline to in vivo trajectory data of macrophages and neutrophils, extracted from zebrafish that had undergone tail transection. We find that macrophage and neutrop...

Edward Codling

Gabriel Rosser

There are various cases of animal movement where behaviour broadly switches between two modes of operation, corresponding to a long-distance movement state and a resting or local movement state. Here, a mathematical description of this process is formulated, adapted from Friedrich et al. (Phys Rev E, 74:041103, 2006b). The approach allows the specification any running or waiting time distribution along with any angular and speed distributions. The resulting system of integro-partial differential equations is tumultuous, and therefore, it is necessary to both simplify and derive summary statistics.An expression for themean squared displacement is derived, which shows good agreement with experimental data from the bacterium Escherichia coli and the gull Larus fuscus. Finally, a large time diffusive approximation is considered via a Cattaneo approximation (Hillen in Discrete Continuous Dyn Syst Ser B, 5:299–318, 2003). This leads to the novel result that the effective diffusion constan...

BMC Biophysics

Joanna Trylska

RELATED PAPERS

Diversidad Cultural-Net

EUV, X-Ray, and Neutron Optics and Sources

American Ethnologist

Ana Sofía Alvarado Hurtado

Revista Da Faculdade De Direito Da Ufg

Beatriz Pires

Social-Medicinsk tidskrift

Aguas Subterrâneas

alexandre miller

Haifa Tahlawi

Daniel Melnikov

murna muzaifa

Simon Oosting

Anton Surviyanto

Philomena George

Fazeenah Hameed

Maria Nikijuluw

Kelly Lewis

International Journal of Antimicrobial Agents

Nutrición hospitalaria

María Pilar Cereceda Bujaico

Zero-a-Seis

Peterson Rigato Da Silva

Perspectives in Education

Tamar Meskin

sandra graciela oyarzo

Christine Haaland

Dyes and Pigments

José Toca-herera

FEBS Open Bio

mohammad asghar

Journal of AIDS & clinical research

Luis Espinoza

Gender and Education

Scheherazade Sadegh-Nasseri

International journal of economics, business and management research

Riszty Budi Deviana

Revista Brasileira de Enfermagem

Carlos Eduardo Dos Santos

Publisher: Emerald Publication Name: PSU Research Review

pallabi siddiqua

Water Science and Technology

Bakunzibake Philippe

North American Archaeologist

Evan Pellegrini

Movement Disorders

Stephan Bohlhalter

RELATED TOPICS

  •   We're Hiring!
  •   Help Center
  • Find new research papers in:
  • Health Sciences
  • Earth Sciences
  • Cognitive Science
  • Mathematics
  • Computer Science
  • Academia ©2024

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

  • Knowledge Base

Methodology

  • Confounding Variables | Definition, Examples & Controls

Confounding Variables | Definition, Examples & Controls

Published on May 29, 2020 by Lauren Thomas . Revised on June 22, 2023.

In research that investigates a potential cause-and-effect relationship, a confounding variable is an unmeasured third variable that influences both the supposed cause and the supposed effect.

It’s important to consider potential confounding variables and account for them in your research design to ensure your results are valid . Left unchecked, confoudning variables can introduce many research biases to your work, causing you to misinterpret your results.

Table of contents

What is a confounding variable, why confounding variables matter, how to reduce the impact of confounding variables, other interesting articles, frequently asked questions about confounding variables.

Confounding variables (a.k.a. confounders or confounding factors) are a type of extraneous variable that are related to a study’s independent and dependent variables . A variable must meet two conditions to be a confounder:

  • It must be correlated with the independent variable. This may be a causal relationship, but it does not have to be.
  • It must be causally related to the dependent variable.

Example of a confounding variable

Prevent plagiarism. Run a free check.

To ensure the internal validity of your research, you must account for confounding variables. If you fail to do so, your results may not reflect the actual relationship between the variables that you are interested in, biasing your results.

For instance, you may find a cause-and-effect relationship that does not actually exist, because the effect you measure is caused by the confounding variable (and not by your independent variable). This can lead to omitted variable bias or placebo effects , among other biases.

Even if you correctly identify a cause-and-effect relationship, confounding variables can result in over- or underestimating the impact of your independent variable on your dependent variable.

There are several methods of accounting for confounding variables. You can use the following methods when studying any type of subjects— humans, animals, plants, chemicals, etc. Each method has its own advantages and disadvantages.

Restriction

In this method, you restrict your treatment group by only including subjects with the same values of potential confounding factors.

Since these values do not differ among the subjects of your study, they cannot correlate with your independent variable and thus cannot confound the cause-and-effect relationship you are studying.

  • Relatively easy to implement
  • Restricts your sample a great deal
  • You might fail to consider other potential confounders

In this method, you select a comparison group that matches with the treatment group. Each member of the comparison group should have a counterpart in the treatment group with the same values of potential confounders, but different independent variable values.

This allows you to eliminate the possibility that differences in confounding variables cause the variation in outcomes between the treatment and comparison group. If you have accounted for any potential confounders, you can thus conclude that the difference in the independent variable must be the cause of the variation in the dependent variable.

  • Allows you to include more subjects than restriction
  • Can prove difficult to implement since you need pairs of subjects that match on every potential confounding variable
  • Other variables that you cannot match on might also be confounding variables

Statistical control

If you have already collected the data, you can include the possible confounders as control variables in your regression models ; in this way, you will control for the impact of the confounding variable.

Any effect that the potential confounding variable has on the dependent variable will show up in the results of the regression and allow you to separate the impact of the independent variable.

  • Easy to implement
  • Can be performed after data collection
  • You can only control for variables that you observe directly, but other confounding variables you have not accounted for might remain

Randomization

Another way to minimize the impact of confounding variables is to randomize the values of your independent variable. For instance, if some of your participants are assigned to a treatment group while others are in a control group , you can randomly assign participants to each group.

Randomization ensures that with a sufficiently large sample, all potential confounding variables—even those you cannot directly observe in your study—will have the same average value between different groups. Since these variables do not differ by group assignment, they cannot correlate with your independent variable and thus cannot confound your study.

Since this method allows you to account for all potential confounding variables, which is nearly impossible to do otherwise, it is often considered to be the best way to reduce the impact of confounding variables.

  • Allows you to account for all possible confounding variables, including ones that you may not observe directly
  • Considered the best method for minimizing the impact of confounding variables
  • Most difficult to carry out
  • Must be implemented prior to beginning data collection
  • You must ensure that only those in the treatment (and not control) group receive the treatment

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Normal distribution
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Ecological validity

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Thomas, L. (2023, June 22). Confounding Variables | Definition, Examples & Controls. Scribbr. Retrieved March 4, 2024, from https://www.scribbr.com/methodology/confounding-variables/

Is this article helpful?

Lauren Thomas

Lauren Thomas

Other students also liked, independent vs. dependent variables | definition & examples, extraneous variables | examples, types & controls, control variables | what are they & why do they matter, what is your plagiarism score.

  • Bipolar Disorder
  • Therapy Center
  • When To See a Therapist
  • Types of Therapy
  • Best Online Therapy
  • Best Couples Therapy
  • Best Family Therapy
  • Managing Stress
  • Sleep and Dreaming
  • Understanding Emotions
  • Self-Improvement
  • Healthy Relationships
  • Student Resources
  • Personality Types
  • Verywell Mind Insights
  • 2023 Verywell Mind 25
  • Mental Health in the Classroom
  • Editorial Process
  • Meet Our Review Board
  • Crisis Support

The Definition of Random Assignment According to Psychology

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

random assignment biology

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

random assignment biology

Materio / Getty Images

Random assignment refers to the use of chance procedures in psychology experiments to ensure that each participant has the same opportunity to be assigned to any given group in a study to eliminate any potential bias in the experiment at the outset. Participants are randomly assigned to different groups, such as the treatment group versus the control group. In clinical research, randomized clinical trials are known as the gold standard for meaningful results.

Simple random assignment techniques might involve tactics such as flipping a coin, drawing names out of a hat, rolling dice, or assigning random numbers to a list of participants. It is important to note that random assignment differs from random selection .

While random selection refers to how participants are randomly chosen from a target population as representatives of that population, random assignment refers to how those chosen participants are then assigned to experimental groups.

Random Assignment In Research

To determine if changes in one variable will cause changes in another variable, psychologists must perform an experiment. Random assignment is a critical part of the experimental design that helps ensure the reliability of the study outcomes.

Researchers often begin by forming a testable hypothesis predicting that one variable of interest will have some predictable impact on another variable.

The variable that the experimenters will manipulate in the experiment is known as the independent variable , while the variable that they will then measure for different outcomes is known as the dependent variable. While there are different ways to look at relationships between variables, an experiment is the best way to get a clear idea if there is a cause-and-effect relationship between two or more variables.

Once researchers have formulated a hypothesis, conducted background research, and chosen an experimental design, it is time to find participants for their experiment. How exactly do researchers decide who will be part of an experiment? As mentioned previously, this is often accomplished through something known as random selection.

Random Selection

In order to generalize the results of an experiment to a larger group, it is important to choose a sample that is representative of the qualities found in that population. For example, if the total population is 60% female and 40% male, then the sample should reflect those same percentages.

Choosing a representative sample is often accomplished by randomly picking people from the population to be participants in a study. Random selection means that everyone in the group stands an equal chance of being chosen to minimize any bias. Once a pool of participants has been selected, it is time to assign them to groups.

By randomly assigning the participants into groups, the experimenters can be fairly sure that each group will have the same characteristics before the independent variable is applied.

Participants might be randomly assigned to the control group , which does not receive the treatment in question. The control group may receive a placebo or receive the standard treatment. Participants may also be randomly assigned to the experimental group , which receives the treatment of interest. In larger studies, there can be multiple treatment groups for comparison.

There are simple methods of random assignment, like rolling the die. However, there are more complex techniques that involve random number generators to remove any human error.

There can also be random assignment to groups with pre-established rules or parameters. For example, if you want to have an equal number of men and women in each of your study groups, you might separate your sample into two groups (by sex) before randomly assigning each of those groups into the treatment group and control group.

Random assignment is essential because it increases the likelihood that the groups are the same at the outset. With all characteristics being equal between groups, other than the application of the independent variable, any differences found between group outcomes can be more confidently attributed to the effect of the intervention.

Example of Random Assignment

Imagine that a researcher is interested in learning whether or not drinking caffeinated beverages prior to an exam will improve test performance. After randomly selecting a pool of participants, each person is randomly assigned to either the control group or the experimental group.

The participants in the control group consume a placebo drink prior to the exam that does not contain any caffeine. Those in the experimental group, on the other hand, consume a caffeinated beverage before taking the test.

Participants in both groups then take the test, and the researcher compares the results to determine if the caffeinated beverage had any impact on test performance.

A Word From Verywell

Random assignment plays an important role in the psychology research process. Not only does this process help eliminate possible sources of bias, but it also makes it easier to generalize the results of a tested sample of participants to a larger population.

Random assignment helps ensure that members of each group in the experiment are the same, which means that the groups are also likely more representative of what is present in the larger population of interest. Through the use of this technique, psychology researchers are able to study complex phenomena and contribute to our understanding of the human mind and behavior.

Lin Y, Zhu M, Su Z. The pursuit of balance: An overview of covariate-adaptive randomization techniques in clinical trials . Contemp Clin Trials. 2015;45(Pt A):21-25. doi:10.1016/j.cct.2015.07.011

Sullivan L. Random assignment versus random selection . In: The SAGE Glossary of the Social and Behavioral Sciences. SAGE Publications, Inc.; 2009. doi:10.4135/9781412972024.n2108

Alferes VR. Methods of Randomization in Experimental Design . SAGE Publications, Inc.; 2012. doi:10.4135/9781452270012

Nestor PG, Schutt RK. Research Methods in Psychology: Investigating Human Behavior. (2nd Ed.). SAGE Publications, Inc.; 2015.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Random Assignment in Psychology (Definition + 40 Examples)

practical psychology logo

Have you ever wondered how researchers discover new ways to help people learn, make decisions, or overcome challenges? A hidden hero in this adventure of discovery is a method called random assignment, a cornerstone in psychological research that helps scientists uncover the truths about the human mind and behavior.

Random Assignment is a process used in research where each participant has an equal chance of being placed in any group within the study. This technique is essential in experiments as it helps to eliminate biases, ensuring that the different groups being compared are similar in all important aspects.

By doing so, researchers can be confident that any differences observed are likely due to the variable being tested, rather than other factors.

In this article, we’ll explore the intriguing world of random assignment, diving into its history, principles, real-world examples, and the impact it has had on the field of psychology.

History of Random Assignment

two women in different conditions

Stepping back in time, we delve into the origins of random assignment, which finds its roots in the early 20th century.

The pioneering mind behind this innovative technique was Sir Ronald A. Fisher , a British statistician and biologist. Fisher introduced the concept of random assignment in the 1920s, aiming to improve the quality and reliability of experimental research .

His contributions laid the groundwork for the method's evolution and its widespread adoption in various fields, particularly in psychology.

Fisher’s groundbreaking work on random assignment was motivated by his desire to control for confounding variables – those pesky factors that could muddy the waters of research findings.

By assigning participants to different groups purely by chance, he realized that the influence of these confounding variables could be minimized, paving the way for more accurate and trustworthy results.

Early Studies Utilizing Random Assignment

Following Fisher's initial development, random assignment started to gain traction in the research community. Early studies adopting this methodology focused on a variety of topics, from agriculture (which was Fisher’s primary field of interest) to medicine and psychology.

The approach allowed researchers to draw stronger conclusions from their experiments, bolstering the development of new theories and practices.

One notable early study utilizing random assignment was conducted in the field of educational psychology. Researchers were keen to understand the impact of different teaching methods on student outcomes.

By randomly assigning students to various instructional approaches, they were able to isolate the effects of the teaching methods, leading to valuable insights and recommendations for educators.

Evolution of the Methodology

As the decades rolled on, random assignment continued to evolve and adapt to the changing landscape of research.

Advances in technology introduced new tools and techniques for implementing randomization, such as computerized random number generators, which offered greater precision and ease of use.

The application of random assignment expanded beyond the confines of the laboratory, finding its way into field studies and large-scale surveys.

Researchers across diverse disciplines embraced the methodology, recognizing its potential to enhance the validity of their findings and contribute to the advancement of knowledge.

From its humble beginnings in the early 20th century to its widespread use today, random assignment has proven to be a cornerstone of scientific inquiry.

Its development and evolution have played a pivotal role in shaping the landscape of psychological research, driving discoveries that have improved lives and deepened our understanding of the human experience.

Principles of Random Assignment

Delving into the heart of random assignment, we uncover the theories and principles that form its foundation.

The method is steeped in the basics of probability theory and statistical inference, ensuring that each participant has an equal chance of being placed in any group, thus fostering fair and unbiased results.

Basic Principles of Random Assignment

Understanding the core principles of random assignment is key to grasping its significance in research. There are three principles: equal probability of selection, reduction of bias, and ensuring representativeness.

The first principle, equal probability of selection , ensures that every participant has an identical chance of being assigned to any group in the study. This randomness is crucial as it mitigates the risk of bias and establishes a level playing field.

The second principle focuses on the reduction of bias . Random assignment acts as a safeguard, ensuring that the groups being compared are alike in all essential aspects before the experiment begins.

This similarity between groups allows researchers to attribute any differences observed in the outcomes directly to the independent variable being studied.

Lastly, ensuring representativeness is a vital principle. When participants are assigned randomly, the resulting groups are more likely to be representative of the larger population.

This characteristic is crucial for the generalizability of the study’s findings, allowing researchers to apply their insights broadly.

Theoretical Foundation

The theoretical foundation of random assignment lies in probability theory and statistical inference .

Probability theory deals with the likelihood of different outcomes, providing a mathematical framework for analyzing random phenomena. In the context of random assignment, it helps in ensuring that each participant has an equal chance of being placed in any group.

Statistical inference, on the other hand, allows researchers to draw conclusions about a population based on a sample of data drawn from that population. It is the mechanism through which the results of a study can be generalized to a broader context.

Random assignment enhances the reliability of statistical inferences by reducing biases and ensuring that the sample is representative.

Differentiating Random Assignment from Random Selection

It’s essential to distinguish between random assignment and random selection, as the two terms, while related, have distinct meanings in the realm of research.

Random assignment refers to how participants are placed into different groups in an experiment, aiming to control for confounding variables and help determine causes.

In contrast, random selection pertains to how individuals are chosen to participate in a study. This method is used to ensure that the sample of participants is representative of the larger population, which is vital for the external validity of the research.

While both methods are rooted in randomness and probability, they serve different purposes in the research process.

Understanding the theories, principles, and distinctions of random assignment illuminates its pivotal role in psychological research.

This method, anchored in probability theory and statistical inference, serves as a beacon of reliability, guiding researchers in their quest for knowledge and ensuring that their findings stand the test of validity and applicability.

Methodology of Random Assignment

woman sleeping with a brain monitor

Implementing random assignment in a study is a meticulous process that involves several crucial steps.

The initial step is participant selection, where individuals are chosen to partake in the study. This stage is critical to ensure that the pool of participants is diverse and representative of the population the study aims to generalize to.

Once the pool of participants has been established, the actual assignment process begins. In this step, each participant is allocated randomly to one of the groups in the study.

Researchers use various tools, such as random number generators or computerized methods, to ensure that this assignment is genuinely random and free from biases.

Monitoring and adjusting form the final step in the implementation of random assignment. Researchers need to continuously observe the groups to ensure that they remain comparable in all essential aspects throughout the study.

If any significant discrepancies arise, adjustments might be necessary to maintain the study’s integrity and validity.

Tools and Techniques Used

The evolution of technology has introduced a variety of tools and techniques to facilitate random assignment.

Random number generators, both manual and computerized, are commonly used to assign participants to different groups. These generators ensure that each individual has an equal chance of being placed in any group, upholding the principle of equal probability of selection.

In addition to random number generators, researchers often use specialized computer software designed for statistical analysis and experimental design.

These software programs offer advanced features that allow for precise and efficient random assignment, minimizing the risk of human error and enhancing the study’s reliability.

Ethical Considerations

The implementation of random assignment is not devoid of ethical considerations. Informed consent is a fundamental ethical principle that researchers must uphold.

Informed consent means that every participant should be fully informed about the nature of the study, the procedures involved, and any potential risks or benefits, ensuring that they voluntarily agree to participate.

Beyond informed consent, researchers must conduct a thorough risk and benefit analysis. The potential benefits of the study should outweigh any risks or harms to the participants.

Safeguarding the well-being of participants is paramount, and any study employing random assignment must adhere to established ethical guidelines and standards.

Conclusion of Methodology

The methodology of random assignment, while seemingly straightforward, is a multifaceted process that demands precision, fairness, and ethical integrity. From participant selection to assignment and monitoring, each step is crucial to ensure the validity of the study’s findings.

The tools and techniques employed, coupled with a steadfast commitment to ethical principles, underscore the significance of random assignment as a cornerstone of robust psychological research.

Benefits of Random Assignment in Psychological Research

The impact and importance of random assignment in psychological research cannot be overstated. It is fundamental for ensuring the study is accurate, allowing the researchers to determine if their study actually caused the results they saw, and making sure the findings can be applied to the real world.

Facilitating Causal Inferences

When participants are randomly assigned to different groups, researchers can be more confident that the observed effects are due to the independent variable being changed, and not other factors.

This ability to determine the cause is called causal inference .

This confidence allows for the drawing of causal relationships, which are foundational for theory development and application in psychology.

Ensuring Internal Validity

One of the foremost impacts of random assignment is its ability to enhance the internal validity of an experiment.

Internal validity refers to the extent to which a researcher can assert that changes in the dependent variable are solely due to manipulations of the independent variable , and not due to confounding variables.

By ensuring that each participant has an equal chance of being in any condition of the experiment, random assignment helps control for participant characteristics that could otherwise complicate the results.

Enhancing Generalizability

Beyond internal validity, random assignment also plays a crucial role in enhancing the generalizability of research findings.

When done correctly, it ensures that the sample groups are representative of the larger population, so can allow researchers to apply their findings more broadly.

This representative nature is essential for the practical application of research, impacting policy, interventions, and psychological therapies.

Limitations of Random Assignment

Potential for implementation issues.

While the principles of random assignment are robust, the method can face implementation issues.

One of the most common problems is logistical constraints. Some studies, due to their nature or the specific population being studied, find it challenging to implement random assignment effectively.

For instance, in educational settings, logistical issues such as class schedules and school policies might stop the random allocation of students to different teaching methods .

Ethical Dilemmas

Random assignment, while methodologically sound, can also present ethical dilemmas.

In some cases, withholding a potentially beneficial treatment from one of the groups of participants can raise serious ethical questions, especially in medical or clinical research where participants' well-being might be directly affected.

Researchers must navigate these ethical waters carefully, balancing the pursuit of knowledge with the well-being of participants.

Generalizability Concerns

Even when implemented correctly, random assignment does not always guarantee generalizable results.

The types of people in the participant pool, the specific context of the study, and the nature of the variables being studied can all influence the extent to which the findings can be applied to the broader population.

Researchers must be cautious in making broad generalizations from studies, even those employing strict random assignment.

Practical and Real-World Limitations

In the real world, many variables cannot be manipulated for ethical or practical reasons, limiting the applicability of random assignment.

For instance, researchers cannot randomly assign individuals to different levels of intelligence, socioeconomic status, or cultural backgrounds.

This limitation necessitates the use of other research designs, such as correlational or observational studies , when exploring relationships involving such variables.

Response to Critiques

In response to these critiques, people in favor of random assignment argue that the method, despite its limitations, remains one of the most reliable ways to establish cause and effect in experimental research.

They acknowledge the challenges and ethical considerations but emphasize the rigorous frameworks in place to address them.

The ongoing discussion around the limitations and critiques of random assignment contributes to the evolution of the method, making sure it is continuously relevant and applicable in psychological research.

While random assignment is a powerful tool in experimental research, it is not without its critiques and limitations. Implementation issues, ethical dilemmas, generalizability concerns, and real-world limitations can pose significant challenges.

However, the continued discourse and refinement around these issues underline the method's enduring significance in the pursuit of knowledge in psychology.

By being careful with how we do things and doing what's right, random assignment stays a really important part of studying how people act and think.

Real-World Applications and Examples

man on a treadmill

Random assignment has been employed in many studies across various fields of psychology, leading to significant discoveries and advancements.

Here are some real-world applications and examples illustrating the diversity and impact of this method:

  • Medicine and Health Psychology: Randomized Controlled Trials (RCTs) are the gold standard in medical research. In these studies, participants are randomly assigned to either the treatment or control group to test the efficacy of new medications or interventions.
  • Educational Psychology: Studies in this field have used random assignment to explore the effects of different teaching methods, classroom environments, and educational technologies on student learning and outcomes.
  • Cognitive Psychology: Researchers have employed random assignment to investigate various aspects of human cognition, including memory, attention, and problem-solving, leading to a deeper understanding of how the mind works.
  • Social Psychology: Random assignment has been instrumental in studying social phenomena, such as conformity, aggression, and prosocial behavior, shedding light on the intricate dynamics of human interaction.

Let's get into some specific examples. You'll need to know one term though, and that is "control group." A control group is a set of participants in a study who do not receive the treatment or intervention being tested , serving as a baseline to compare with the group that does, in order to assess the effectiveness of the treatment.

  • Smoking Cessation Study: Researchers used random assignment to put participants into two groups. One group received a new anti-smoking program, while the other did not. This helped determine if the program was effective in helping people quit smoking.
  • Math Tutoring Program: A study on students used random assignment to place them into two groups. One group received additional math tutoring, while the other continued with regular classes, to see if the extra help improved their grades.
  • Exercise and Mental Health: Adults were randomly assigned to either an exercise group or a control group to study the impact of physical activity on mental health and mood.
  • Diet and Weight Loss: A study randomly assigned participants to different diet plans to compare their effectiveness in promoting weight loss and improving health markers.
  • Sleep and Learning: Researchers randomly assigned students to either a sleep extension group or a regular sleep group to study the impact of sleep on learning and memory.
  • Classroom Seating Arrangement: Teachers used random assignment to place students in different seating arrangements to examine the effect on focus and academic performance.
  • Music and Productivity: Employees were randomly assigned to listen to music or work in silence to investigate the effect of music on workplace productivity.
  • Medication for ADHD: Children with ADHD were randomly assigned to receive either medication, behavioral therapy, or a placebo to compare treatment effectiveness.
  • Mindfulness Meditation for Stress: Adults were randomly assigned to a mindfulness meditation group or a waitlist control group to study the impact on stress levels.
  • Video Games and Aggression: A study randomly assigned participants to play either violent or non-violent video games and then measured their aggression levels.
  • Online Learning Platforms: Students were randomly assigned to use different online learning platforms to evaluate their effectiveness in enhancing learning outcomes.
  • Hand Sanitizers in Schools: Schools were randomly assigned to use hand sanitizers or not to study the impact on student illness and absenteeism.
  • Caffeine and Alertness: Participants were randomly assigned to consume caffeinated or decaffeinated beverages to measure the effects on alertness and cognitive performance.
  • Green Spaces and Well-being: Neighborhoods were randomly assigned to receive green space interventions to study the impact on residents’ well-being and community connections.
  • Pet Therapy for Hospital Patients: Patients were randomly assigned to receive pet therapy or standard care to assess the impact on recovery and mood.
  • Yoga for Chronic Pain: Individuals with chronic pain were randomly assigned to a yoga intervention group or a control group to study the effect on pain levels and quality of life.
  • Flu Vaccines Effectiveness: Different groups of people were randomly assigned to receive either the flu vaccine or a placebo to determine the vaccine’s effectiveness.
  • Reading Strategies for Dyslexia: Children with dyslexia were randomly assigned to different reading intervention strategies to compare their effectiveness.
  • Physical Environment and Creativity: Participants were randomly assigned to different room setups to study the impact of physical environment on creative thinking.
  • Laughter Therapy for Depression: Individuals with depression were randomly assigned to laughter therapy sessions or control groups to assess the impact on mood.
  • Financial Incentives for Exercise: Participants were randomly assigned to receive financial incentives for exercising to study the impact on physical activity levels.
  • Art Therapy for Anxiety: Individuals with anxiety were randomly assigned to art therapy sessions or a waitlist control group to measure the effect on anxiety levels.
  • Natural Light in Offices: Employees were randomly assigned to workspaces with natural or artificial light to study the impact on productivity and job satisfaction.
  • School Start Times and Academic Performance: Schools were randomly assigned different start times to study the effect on student academic performance and well-being.
  • Horticulture Therapy for Seniors: Older adults were randomly assigned to participate in horticulture therapy or traditional activities to study the impact on cognitive function and life satisfaction.
  • Hydration and Cognitive Function: Participants were randomly assigned to different hydration levels to measure the impact on cognitive function and alertness.
  • Intergenerational Programs: Seniors and young people were randomly assigned to intergenerational programs to study the effects on well-being and cross-generational understanding.
  • Therapeutic Horseback Riding for Autism: Children with autism were randomly assigned to therapeutic horseback riding or traditional therapy to study the impact on social communication skills.
  • Active Commuting and Health: Employees were randomly assigned to active commuting (cycling, walking) or passive commuting to study the effect on physical health.
  • Mindful Eating for Weight Management: Individuals were randomly assigned to mindful eating workshops or control groups to study the impact on weight management and eating habits.
  • Noise Levels and Learning: Students were randomly assigned to classrooms with different noise levels to study the effect on learning and concentration.
  • Bilingual Education Methods: Schools were randomly assigned different bilingual education methods to compare their effectiveness in language acquisition.
  • Outdoor Play and Child Development: Children were randomly assigned to different amounts of outdoor playtime to study the impact on physical and cognitive development.
  • Social Media Detox: Participants were randomly assigned to a social media detox or regular usage to study the impact on mental health and well-being.
  • Therapeutic Writing for Trauma Survivors: Individuals who experienced trauma were randomly assigned to therapeutic writing sessions or control groups to study the impact on psychological well-being.
  • Mentoring Programs for At-risk Youth: At-risk youth were randomly assigned to mentoring programs or control groups to assess the impact on academic achievement and behavior.
  • Dance Therapy for Parkinson’s Disease: Individuals with Parkinson’s disease were randomly assigned to dance therapy or traditional exercise to study the effect on motor function and quality of life.
  • Aquaponics in Schools: Schools were randomly assigned to implement aquaponics programs to study the impact on student engagement and environmental awareness.
  • Virtual Reality for Phobia Treatment: Individuals with phobias were randomly assigned to virtual reality exposure therapy or traditional therapy to compare effectiveness.
  • Gardening and Mental Health: Participants were randomly assigned to engage in gardening or other leisure activities to study the impact on mental health and stress reduction.

Each of these studies exemplifies how random assignment is utilized in various fields and settings, shedding light on the multitude of ways it can be applied to glean valuable insights and knowledge.

Real-world Impact of Random Assignment

old lady gardening

Random assignment is like a key tool in the world of learning about people's minds and behaviors. It’s super important and helps in many different areas of our everyday lives. It helps make better rules, creates new ways to help people, and is used in lots of different fields.

Health and Medicine

In health and medicine, random assignment has helped doctors and scientists make lots of discoveries. It’s a big part of tests that help create new medicines and treatments.

By putting people into different groups by chance, scientists can really see if a medicine works.

This has led to new ways to help people with all sorts of health problems, like diabetes, heart disease, and mental health issues like depression and anxiety.

Schools and education have also learned a lot from random assignment. Researchers have used it to look at different ways of teaching, what kind of classrooms are best, and how technology can help learning.

This knowledge has helped make better school rules, develop what we learn in school, and find the best ways to teach students of all ages and backgrounds.

Workplace and Organizational Behavior

Random assignment helps us understand how people act at work and what makes a workplace good or bad.

Studies have looked at different kinds of workplaces, how bosses should act, and how teams should be put together. This has helped companies make better rules and create places to work that are helpful and make people happy.

Environmental and Social Changes

Random assignment is also used to see how changes in the community and environment affect people. Studies have looked at community projects, changes to the environment, and social programs to see how they help or hurt people’s well-being.

This has led to better community projects, efforts to protect the environment, and programs to help people in society.

Technology and Human Interaction

In our world where technology is always changing, studies with random assignment help us see how tech like social media, virtual reality, and online stuff affect how we act and feel.

This has helped make better and safer technology and rules about using it so that everyone can benefit.

The effects of random assignment go far and wide, way beyond just a science lab. It helps us understand lots of different things, leads to new and improved ways to do things, and really makes a difference in the world around us.

From making healthcare and schools better to creating positive changes in communities and the environment, the real-world impact of random assignment shows just how important it is in helping us learn and make the world a better place.

So, what have we learned? Random assignment is like a super tool in learning about how people think and act. It's like a detective helping us find clues and solve mysteries in many parts of our lives.

From creating new medicines to helping kids learn better in school, and from making workplaces happier to protecting the environment, it’s got a big job!

This method isn’t just something scientists use in labs; it reaches out and touches our everyday lives. It helps make positive changes and teaches us valuable lessons.

Whether we are talking about technology, health, education, or the environment, random assignment is there, working behind the scenes, making things better and safer for all of us.

In the end, the simple act of putting people into groups by chance helps us make big discoveries and improvements. It’s like throwing a small stone into a pond and watching the ripples spread out far and wide.

Thanks to random assignment, we are always learning, growing, and finding new ways to make our world a happier and healthier place for everyone!

Related posts:

  • 19+ Experimental Design Examples (Methods + Types)
  • Cluster Sampling vs Stratified Sampling
  • 41+ White Collar Job Examples (Salary + Path)
  • 47+ Blue Collar Job Examples (Salary + Path)
  • McDonaldization of Society (Definition + Examples)

Reference this article:

About The Author

Photo of author

Free Personality Test

Free Personality Quiz

Free Memory Test

Free Memory Test

Free IQ Test

Free IQ Test

PracticalPie.com is a participant in the Amazon Associates Program. As an Amazon Associate we earn from qualifying purchases.

Follow Us On:

Youtube Facebook Instagram X/Twitter

Psychology Resources

Developmental

Personality

Relationships

Psychologists

Serial Killers

Psychology Tests

Personality Quiz

Memory Test

Depression test

Type A/B Personality Test

© PracticalPsychology. All rights reserved

Privacy Policy | Terms of Use

IMAGES

  1. Biology Archive

    random assignment biology

  2. Border Assignment Design For Front Page Easy For Biology

    random assignment biology

  3. Biology Projects, Front Page Design, Cover Pages, Book Cover, Learn

    random assignment biology

  4. PPT

    random assignment biology

  5. Unit 5 cell biology assignment

    random assignment biology

  6. A-Level Biology revision notes, videos and more for AQA. Also offering

    random assignment biology

VIDEO

  1. Random Assignment

  2. Assignment: BIOLOGY CHAPTER 1& CHAPTER 2

  3. random sampling & assignment

  4. Random Team Assignment- 2023 Topps World Baseball Classic #10 Full Case Random Country!

  5. Front Page Design for School Project/Assignment : BIOLOGY 🌷#shorts

  6. Neet Assignment!! Neet Assignment biology ch. 1 living world 2024

COMMENTS

  1. Random Assignment in Experiments

    Random sampling (also called probability sampling or random selection) is a way of selecting members of a population to be included in your study. In contrast, random assignment is a way of sorting the sample participants into control and experimental groups. While random sampling is used in many types of studies, random assignment is only used ...

  2. Random sampling vs. random assignment (scope of inference)

    1. All of the students select a marble from a bag, and the 50 students with green marbles participate. 2. Jared asks 50 of his friends to participate in the study. 3. The names of all of the students in the school are put in a bowl and 50 names are drawn. 4.

  3. Random assignment

    Random assignment or random placement is an experimental technique for assigning human participants or animal subjects to different groups in an experiment (e.g., a treatment group versus a control group) using randomization, such as by a chance procedure (e.g., flipping a coin) or a random number generator. This ensures that each participant or subject has an equal chance of being placed in ...

  4. Guide to Experimental Design

    Table of contents. Step 1: Define your variables. Step 2: Write your hypothesis. Step 3: Design your experimental treatments. Step 4: Assign your subjects to treatment groups. Step 5: Measure your dependent variable. Other interesting articles. Frequently asked questions about experiments.

  5. Random Assignment in Experiments

    Random sampling is a process for obtaining a sample that accurately represents a population. Random assignment uses a chance process to assign subjects to experimental groups. Using random assignment requires that the experimenters can control the group assignment for all study subjects. For our study, we must be able to assign our participants ...

  6. Natural experiment

    In particular, IV analyses depend on the assumption that subjects were effectively randomized, even if the randomization was accidental (in the case of an administrative policy change or exposure to a natural disaster) and adherence to random assignment was low. IV methods can be used to control for confounding in observational studies, to control for confounding due to noncompliance, and to ...

  7. Blinding and Randomization

    Simple random sampling is defined as the process of selecting subjects from a population such that just the following two criteria are satisfied: The probability of assignment to any of the experimental groups is equal for each subject. The assignment of one subject to a group does not affect the assignment of any other subject to that same group.

  8. How to Do Random Allocation (Randomization)

    Due to the nature of Excel, if there is a change, it creates a new random number accordingly. If we input any number instead of '2' in the orange-colored cell and click the 'enter key,' it creates new random sequences (Fig. 1). The sequences are the result of simple randomization.

  9. Challenges and Dilemmas in Implementing Random Assignment in

    Consideration of challenges encountered in implementing random assignment suggests that 1) researcher communication with program staff improves compliance, but may not overcome the need for learning through experience; 2) in keeping with arguments in favor of random assignment-based research, random assignment may control for diverse selection ...

  10. Single, Double & Triple Blind Study

    A double-blind study withholds each subject's group assignment from both the participant and the researcher performing the experiment. If participants know which group they are assigned to, there is a risk that they might change their behavior in a way that would influence the results. This can lead to a few types of research bias ...

  11. Random walk models in biology

    Abstract. Mathematical modelling of the movement of animals, micro-organisms and cells is of great relevance in the fields of biology, ecology and medicine. Movement models can take many different forms, but the most widely used are based on the extensions of simple random walk processes. In this review paper, our aim is twofold: to introduce ...

  12. How to do random assignments in an experiment

    Explanation: . The only random procedure here is the coin flip. In expectation, the coin flip ensures that no background variables influence treatment assignment whereas the other examples either have nothing to do with random assignment (e.g. nationally representative sample) or completely contradict the purpose of random assignment (e.g. assigning the treatment based on who needs it the most).

  13. Blocked Randomization with Randomly Selected Block Sizes

    A planned enrollment of 250 participants, 50 per study site, is to be randomly assigned to the two intervention arms. Below, a computer algorithm written in SAS ® (Cary, NC) is presented for performing a block randomization with randomly selected block sizes of 4, 8 and 12 ( Figure 1 ). The macro generates 15 randomized block allocations each ...

  14. Random Assignment in Experiments

    Random Assignment in Experiments | Introduction & Examples. Published on 6 May 2022 by Pritha Bhandari.Revised on 13 February 2023. In experimental research, random assignment is a way of placing participants from your sample into different treatment groups using randomisation. With simple random assignment, every member of the sample has a known or equal chance of being placed in a control ...

  15. Random Assignment in Psychology: Definition & Examples

    Random selection (also called probability sampling or random sampling) is a way of randomly selecting members of a population to be included in your study. On the other hand, random assignment is a way of sorting the sample participants into control and treatment groups. Random selection ensures that everyone in the population has an equal ...

  16. random assignment & designs Flashcards

    Terms in this set (9) randomization scheme for those that possess the characteristic, and different randomization scheme for those that do not. can conclude difference between groups over time. Study with Quizlet and memorize flashcards containing terms like random sampling, random assignment, Free random assignment and more.

  17. 7.6: Genetic Variation

    This exchange of genetic material can happen many times within the same pair of homologous chromosomes, creating unique combinations of genes. This process is also known as recombination. Figure 7.6.2 7.6. 2 : Crossing-over. A maternal strand of DNA is shown in red. A paternal strand of DNA is shown in blue.

  18. (PDF) Random walk models in biology

    Random walk models in biology. Edward Codling. 2008, Journal of the …. Mathematical modelling of the movement of animals, micro-organisms and cells is of great relevance in the fields of biology, ecology and medicine. Movement models can take many different forms, but the most widely used are based on the extensions of simple random walk ...

  19. Confounding Variables

    Confounding variables (a.k.a. confounders or confounding factors) are a type of extraneous variable that are related to a study's independent and dependent variables. A variable must meet two conditions to be a confounder: It must be correlated with the independent variable. This may be a causal relationship, but it does not have to be.

  20. 5.02 Evolutionary Relationships

    06 08 immune system mystery assignment; Kami Export - Brianna Avalos - Copy of Meiosis Phases Graphic Organizer ... Downloaded by Random User (hilgasjimbo@gmail) 1 67 33 2 82 18 3 84 16 4 85 15 5 100 0 ... Kami Export - Biology. Biology 97% (237) 7. Evolution and selection pogil key 1617 230528 080443. Biology 97% (66) 7.

  21. The Definition of Random Assignment In Psychology

    Trending Videos. Random assignment refers to the use of chance procedures in psychology experiments to ensure that each participant has the same opportunity to be assigned to any given group in a study to eliminate any potential bias in the experiment at the outset. Participants are randomly assigned to different groups, such as the treatment ...

  22. Random Assignment in Psychology (Definition + 40 Examples)

    Stepping back in time, we delve into the origins of random assignment, which finds its roots in the early 20th century. The pioneering mind behind this innovative technique was Sir Ronald A. Fisher, a British statistician and biologist.Fisher introduced the concept of random assignment in the 1920s, aiming to improve the quality and reliability of experimental research.

  23. A biology student wanted to determine if using fertilizer would promote

    Random assignment is an important aspect of experimental design. In this scenario, the biology student randomly assigned 100 plants to either a group that received fertilizer or a group that did not. Random assignment helps ensure that the variability between each plant is spread evenly across both groups. However, it does not guarantee that ...