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Types of variance, common cause variation, common cause variation examples, special cause variation, special cause variation example, choose the right program, common cause variation vs. special cause variation.

Common Cause Variation Vs. Special Cause Variation

Every piece of data which is measured will show some degree of variation: no matter how much we try, we could never attain identical results for two different situations - each result will be different, even if the difference is slight. Variation may be defined as “the numerical value used to indicate how widely individuals in a group vary.” 

In other words, variance gives us an idea of how data is distributed about an expected value or the mean. If you attain a variance of zero, it indicates that your results are identical - an uncommon condition. A high variance shows that the data points are spread out from each other—and the mean, while a smaller variation indicates that the data points are closer to the mean. Variance is always nonnegative.

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Change is inevitable, even in statistics. You’ll need to know what kind of variation affects your process because the course of action you take will depend on the type of variance. There are two types of Variance: Common Cause Variation and Special Cause Variation. You’ll need to know about Common Causes Variation vs Special Causes Variation because they are two subjects that are tested on the PMP Certification  and CAPM Certification exams. 

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Common Cause Variation, also referred to as “Natural Problems, “Noise,” and “Random Cause” was a term coined by Harry Alpert in 1947. Common causes of variance are the usual quantifiable and historical variations in a system that are natural. Though variance is a problem, it is an inherent part of a process—variance will eventually creep in, and it is not much you can do about it. Specific actions cannot be taken to prevent this failure from occurring. It is ongoing, consistent, and predictable.

Characteristics of common causes variation are:

  • Variation predictable probabilistically
  • Phenomena that are active within the system
  • Variation within a historical experience base which is not regular
  • Lack of significance in individual high and low values

This variation usually lies within three standard deviations from the mean where 99.73% of values are expected to be found. On a control chart, they are indicated by a few random points that are within the control limit. These kinds of variations will require management action since there can be no immediate process to rectify it. You will have to make a fundamental change to reduce the number of common causes of variation. If there are only common causes of variation on your chart, your process is said to be “statistically stable.”

When this term is applied to your chart, the chart itself becomes fairly stable. Your project will have no major changes, and you will be able to continue process execution hassle-free.

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Consider an employee who takes a little longer than usual to complete a specific task. He is given two days to do a task, and instead, he takes two and a half days; this is considered a common cause variation. His completion time would not have deviated very much from the mean since you would have had to consider the fact that he could submit it a little late.

Here’s another example: you estimate 20 minutes to get ready and ten minutes to get to work. Instead, you take five minutes extra to get ready because you had to pack lunch and 15 additional minutes to get to work because of traffic. 

Other examples that relate to projects are inappropriate procedures, which can include the lack of clearly defined standard procedures, poor working conditions, measurement errors, normal wear and tear, computer response times, etc. These are all common cause variation.

Special Cause Variation, on the other hand, refers to unexpected glitches that affect a process. The term Special Cause Variation was coined by W. Edwards Deming and is also known as an “Assignable Cause.” These are variations that were not observed previously and are unusual, non-quantifiable variations.

These causes are sporadic, and they are a result of a specific change that is brought about in a process resulting in a chaotic problem. It is not usually part of your normal process and occurs out of the blue. Causes are usually related to some defect in the system or method. However, this failure can be corrected by making changes to affected methods, components, or processes.

Characteristics of special cause variation are:

  • New and unanticipated or previously neglected episode within the system
  • This kind of variation is usually unpredictable and even problematic
  • The variation has never happened before and is thus outside the historical experience base

On a control chart, the points lie beyond the preferred control limit or even as random points within the control limit. Once identified on a chart, this type of problem needs to be found and addressed immediately you can help prevent it from recurring.

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Let’s say you are driving to work, and you estimate arrival in 10 minutes every day. One day, it took you 20 minutes to arrive at work because you were caught in the traffic from an accident zone and were held up.

Examples relating to project management are if machine malfunctions, computer crashes, there is a power cut, etc. These kinds of random things that can happen during a project are examples of special cause variation.

One way to evaluate a project’s health is to track the difference between the original project plan and what is happening. The use of control charts helps to differentiate between the common cause variation and the special cause variation, making the process of making changes and amends easier.

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special cause variation vs common

What is the variation?

Whatever measurement we take, there is always a variation between these measurements. No two items or measurements are precisely the same.

The problem with the variation is that it is the enemy of quality. Variation and quality do not go hand in hand. Variation reduction is one of the significant challenges of quality professionals.

Two types of variation, and why is it important to differentiate?

When dealing with variation, the challenge quality professionals face when to act and when not to act. Because if you act on each and every variation in the process and adjust the process, this will be a never-ending process. Dr. Deming called this "tempering the process." Rather than improving the quality, tempering, in fact, reduces the quality. Deming demonstrated the effect of tempering with the help of a funnel experiment.

The causes of variation can be classified into two categories:

  • Common Causes
  • Special Causes

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special cause variation vs common

Common Cause Vs Special Cause: Types of Variation

Common cause variation  is the natural variation in the process. It is a part of the process. There are "many" causes of this type of variation, and it is not easy to identify and remove these. You will need to live with them unless drastic action is taken, such as process re-engineering.

Common causes are also called  n atural causes, noise, non-assignable and random causes .

Special cause variation,  on the other hand, is the unexpected variation in the process. There is a specific cause that can be assigned to the variation. For that reason, this is also called as the  assignable cause . You are required to take action to address these variations.

Special causes are also called  assignable causes .

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Control Charts to identify special causes

If the measurements of a process are normally distributed, then there is a 99.73% chance that the measurement will be within plus and minus three standard deviations. This is the basis of control charts . 

If you plot the measurements on a Control Chart , then any measurements which are outside the plus and minus three standard deviation limits are expected to be because of a special cause. These limits are called as the Upper Control Limit (UCL) and the Lower Control Limits (LCL), Once you get such measurement, you are expected to investigate, do the root cause analysis , find out the reason for such deviation and take necessary actions.

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Common Cause & Special Cause Variation Explained with Examples

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Common Cause & Special Cause Variation pmp

In any business operation, it is important to ensure consistency in products as well as repeatable results. Managers and workers alike have to be aware of the processes and methods on how to produce consistent outcomes at all costs. However, we cannot deny that producing exactly identical products or results is almost impossible as variance tends to exist. Variation is not necessarily a bad thing as long as it is within the standard of the critical to qualities (CTQs) specification limits.

Process variation is the occurrence when a system deviates from its fixed pattern and produces a result which differs from the usual ones. This is a major key as it concerns the consistencies of the transactional as well as the manufacturing of the business systems. Variation should be evaluated as it portrays the reliability of the business for the customers and stakeholders. Variation may also cost money hence it is crucial to keep variation at bay to prevent too much cost spent on variation. It is crucial to be able to distinguish the types of variance that occur in your business process since it will give the lead on what course of action to take. Mistakes in coming up with an effective reaction plan towards the variance may worsen the processes of the business.

There are two types of process variation which will be further elaborated in this article. The variations are known as common cause variation and special cause variation.

Common Cause Variation Definition

Common cause variation refers to the natural and measurable anomalies that occur in the system or business processes. It naturally exists within the system. While it is true that variance may bring a negative impact to business operations, we cannot escape from this aspect. It is inherent and will always be. In most cases, the common cause variant is constant, regular, and could be predicted within the business operations. The other term used to describe this variation is Natural Problems, Noise, or Random Cause. Common cause variance could be presented and analysed using histogram.

What is Common Cause Variation

There are several distinguishable characteristics of common cause variation. Firstly, the variation pattern is predictable. Common cause variation occurring is also an active event in the operations. it is controlled and is not significantly different from the usual phenomenon.

There are many factors and reasons for common cause variation and it is quite difficult to pinpoint and eliminate them. Some common cause variations are accepted within the business process and operations as long as they are within a tolerable level. Eradicating them is an arduous effort unless a drastic measure is implemented towards the operation.  

Common Cause Variation Examples

There is a wide range of examples for common cause variation. Let’s take driving as an example. Usually, a driver is well aware of their destinations and the conditions of the path to reach the destination. Since they have been regularly using the same road, any defects or problems such as bumps, conditions of the road, and usual traffic are normal. They may not be able to precisely arrive at the destination at the same duration every time due to these common causes. However, the duration to arrive at the destination may not be largely differing day to day.

In terms of project-related variations, some of the examples include technical issues, human errors, downtime, high trafficking, poor computer response times, mistakes in standard procedures, and many more. Some other examples of common causes include poor design of products, outdated systems, and poor maintenance. Inconducive working conditions may also result in to common cause variants which could comprise of ventilation, temperature, humidity, noise, lighting, dirt, and so forth. Errors such as quality control and measurement could also be counted as common cause variation.

Special Cause Variation Definition

On the other hand, special cause variation refers to the unforeseen anomalies or variance that occurs within business operations. This variation, as the name suggests, is special in terms of being rare, having non-quantifiable patterns, and may not have been observed before. It is also known as Assignable Cause. Other opinions also mentioned that special cause variation is not only variance that happens for the first time, a previously overlooked or ignored problem could also be considered a special cause variation.

What is Special Cause Variation

Special cause variation is irregular occurrences and usually happens due to changes that were brought about in the business operations. It is not your mundane defects and may be very unpredictable. Most of the time, special cause variation happens following the flaws within the business processes or mechanism. While it may sound serious and taxing, there are ways to fix this which is by modifying the affected procedures or materials.

One of the characteristics of special cause variation is that it is uncontrolled and hardly predictable. The outcome of special causes variation is significantly different from the usual phenomenon. Since the issues are not predictable, it is usually problematic and may not even be recorded in the historical experience base.

Special Cause Variation Examples

As mentioned earlier, special cause variations are unexpected variants that occur due to factors that may affect the business system or operations. Let’s have an example of a special cause using the same scenario as previously elaborated for common cause variation example. The mentioned defects were common. Now, imagine if there is an unexpected accident that happens on the same road you usually take. Due to this accident, the time for the driver to arrive at the same destination may take longer than normal. Hence this accident is considered as a special cause variation. It is unexpected and results in a significantly different outcome, in this case, a longer time to arrive at the destination.

The example of special cause variation in the manufacturing sector includes environment, materials, manpower, technology, equipment, and many more. In terms of manpower, imagine a new employee is recruited into the team and still lacking in experience. The coaching and instructions should be adapted to consider that the person needs more training to be able to perform their tasks efficiently. Cases where a new supplier is needed in a short amount of time due to issues faced by the existing supplier are also unforeseen hence considered a special cause variation. Natural hazards that are beyond predictions may also be categorized into special cause variation. Some other examples include irregular traffic or fraud attack. An unexpected computer crash or malfunction in some of the components may also be considered as a special cause variation.

Common Cause and Special Cause Variation Detection

Control chart

One of the ways to keep track of common cause and special cause variation is by implementing control charts. When using control charts, the important aspect to be considered is firstly, establishing the average point of measurement. Next, establish the control limits. Usually, there are three standard deviations which are marked above and below the average point earlier. The last step is by determining which points exceed the upper and lower control limits established earlier. The points beyond the limits are special cause variation.

Before we get into the control chart of common cause and special cause variation, let’s have a look at the eight control chart rules first. If a process is stable, the points displayed in the chart will be near the average point and will not exceed the control limits.

However, it should be noted that not all rules are applicable to all types of control charts. That aside, it is quite tough to identify the causes of the patterns since special cause variation may be related to the specific type of processes. The table presented is the general rule that could be applied in most cases but is also subject to changes or differences. Studying the chart should be accompanied by knowledge and experiences in order to pinpoint the reasons for the patterns or variations.

A process is considered stable if special cause variation is not present, even if a common cause exists. A stable operation is important before it could be assessed or being improved. We could look at the stability or instability of the processes as displayed in control charts or run charts .

special cause variation vs common

The points displayed in the chart above are randomly distributed and do not defy any of the eight rules listed earlier. This indicates that the process is stable.

special cause variation vs common

The chart presented above is an example of an unstable process. This is because some of the rules for control chart tests mentioned earlier are violated.

Simply, if the points are randomly distributed and are within the limit, they may be considered as the common cause variation. However, if there is a drastic irregularity or points exceeding the limit, you may want to analyse more into it to determine if it is a special cause variation.

Histogram is a type of bar graph that could be used to present the distribution of occurrences of data. It is easily understandable and analysed. A histogram provides information on the history of the processes done as well as forecasting the future performance of the operations. To ensure the reliability of the data presented in the histogram, it is essential for the process to be stable. As mentioned earlier, although affected by common cause variation, the processes are still considered stable, hence histogram may be used on this occasion, especially if the processes undergo regular measurement and assessment.

The data is considered to be normally distributed if it portrays a “bell” shape in the histogram. The data are grouped around the central value and this cluster is known as variation. There are several other examples of more complicated patterns, such as having several peaks in the histogram or a shortened histogram. Whenever these examples of complex structures appear in the histogram, it is fundamental to look into the data and operations more deeply.

special cause variation vs common

The above bar graph is an example of the histogram with a “bell” shape.

However, it should be noted that just because the histogram displays a “bell” shaped distribution, that does not mean the process is only experiencing common cause variation. A deeper analysis should be done to investigate if there were other underlying factors or causes that lead towards the pattern of the distribution displayed in the histogram.

Countering common cause and special cause variation

Once the causes of the variation have been pinpointed, here comes the attempt to combat and resolve it. Different measures are implemented to counter different types of variation, i.e. common cause variation and special cause variation. Common cause variation is quite tough to be completely eliminated. Drastic or long-term process modification could be used to counter common cause variation. A new method should be introduced and constantly conducted to achieve the long-term goal of eliminating the common cause variation. Some other effects may happen to the operations but as time passes, the cause may be gradually solved. As for special cause variation, it could be countered using contingency plans. Usually, additional processes are implemented into the usual operation in order to counter the special cause variation.

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Volume 8 Supplement 1

Proceedings of Advancing the Methods in Health Quality Improvement Research 2012 Conference

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Understanding and managing variation: three different perspectives

  • Michael E Bowen 1 , 2 , 3 &
  • Duncan Neuhauser 4  

Implementation Science volume  8 , Article number:  S1 ( 2013 ) Cite this article

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Managing variation is essential to quality improvement. Quality improvement is primarily concerned with two types of variation – common-cause variation and special-cause variation. Common-cause variation is random variation present in stable healthcare processes. Special-cause variation is an unpredictable deviation resulting from a cause that is not an intrinsic part of a process. By careful and systematic measurement, it is easier to detect changes that are not random variation.

The approach to managing variation depends on the priorities and perspectives of the improvement leader and the intended generalizability of the results of the improvement effort. Clinical researchers, healthcare managers, and individual patients each have different goals, time horizons, and methodological approaches to managing variation; however, in all cases, the research question should drive study design, data collection, and evaluation. To advance the field of quality improvement, greater understanding of these perspectives and methodologies is needed [ 1 ].

Clinical researcher perspective

The primary goal of traditional randomized controlled trials (RCTs) (ie a comparison of treatment A versus placebo) is to determine treatment or intervention efficacy in a specified population when all else is equal. In this approach, researchers seek to maximize internal validity. Through randomization, researchers seek to balance variation in baseline factors by randomizing patients, clinicians, or organizations to experimental and control groups. Researchers may also increase understanding of variation within a specific study using approaches such as stratification to examine for effect modification. Although the generalizability of outcomes in all research designs is limited by the study population and setting, this can be particularly challenging in traditional RCTs. When inclusion criteria are strict, study populations are not representative of “real world” patients, and the applicability of study findings to clinical practice may be unclear. Traditional RCTs are limited in their ability to evaluate complex processes that are purposefully and continually changing over time because they evaluate interventions in rigorously controlled conditions over fixed time frames [ 2 ]. However, using alternative designs such as hybrid, effectiveness studies discussed in these proceedings or pragmatic RCTs, researchers can rigorously answer a broader range of research questions [ 3 ].

Healthcare manager perspective

Healthcare managers seek to understand and reduce variation in patient populations by monitoring process and outcome measures. They utilize real-time data to learn from and manage variation over time. By comparing past, present, and desired performance, they seek to reduce undesired variation and reinforce desired variation. Additionally, managers often implement best practices and benchmark performance against them. In this process, efficient, time-sensitive evaluations are important. Run charts and Statistical Process Control (SPC) methods leverage the power of repeated measures over time to detect small changes in process stability and increase the statistical power and rapidity with which effects can be detected [ 1 ].

Patient perspective

While the clinical researcher and healthcare manager are interested in understanding and managing variation at a population level, the individual patient wants to know if a particular treatment will allow one to achieve health outcomes similar to those observed in study populations. Although the findings of RCTs help form the foundation of evidence-based practice and managers utilize these findings in population management, they provide less guidance about the likelihood of an individual patient achieving the average benefits observed across a population of patients. Even when RCT findings are statistically significant, many trial participants receive no benefit. In order to understand if group RCT results can be achieved with individual patients, a different methodological approach is needed. “N-of-1 trials” and the longitudinal factorial design of experiments allow patients and providers to systematically evaluate the independent and combined effects of multiple disease management variables on individual health outcomes [ 4 ]. This offers patients and providers the opportunity to collect, analyze, and understand data in real time to improve individual patient outcomes.

Advancing the field of improvement science and increasing our ability to understand and manage variation requires an appreciation of the complementary perspectives held and methodologies utilized by clinical researchers, healthcare managers, and patients. To accomplish this, clinical researchers, healthcare managers, and individual patients each face key challenges.

Recommendations

Clinical researchers are challenged to design studies that yield generalizable outcomes across studies and over time. One potential approach is to anchor research questions in theoretical frameworks to better understand the research problem and relationships among key variables. Additionally, researchers should expand methodological and analytical approaches to leverage the statistical power of multiple observations collected over time. SPC is one such approach. Incorporation of qualitative research and mixed methods can also increase our ability to understand context and the key determinants of variation.

Healthcare managers are challenged to identify best practices and benchmark their processes against them. However, the details of best practices and implementation strategies are rarely described in sufficient detail to allow identification of the key drivers of process improvement and adaption of best practices to local context. By advocating for transparency in process improvement and urging publication of improvement and implementation efforts, healthcare managers can enhance the spread of best practices, facilitate improved benchmarking, and drive continuous healthcare improvement.

Individual patients and providers are challenged to develop the skills needed to understand and manage individual processes and outcomes. As an example, patients with hypertension are often advised to take and titrate medications, modify dietary intake, and increase activity levels in a non-systematic manner. The longitudinal factorial design offers an opportunity to rigorously evaluate the impact of these recommendations, both in isolation and in combination, on disease outcomes [ 1 ]. Patients can utilize paper, smart phone applications, or even electronic health record portals to sequentially record their blood pressures. Patients and providers can then apply simple SPC rules to better understand variation in blood pressure readings and manage their disease [ 5 ].

As clinical researchers, healthcare managers, and individual patients strive to improve healthcare processes and outcomes, each stakeholder brings a different perspective and set of methodological tools to the improvement team. These perspectives and methods are often complementary such that it is not which methodological approach is “best” but rather which approach is best suited to answer the specific research question. By combining these perspectives and developing partnerships with organizational managers, improvement leaders can demonstrate process improvement to key decision makers in the healthcare organization. It is through such partnerships that the future of quality improvement research is likely to find financial support and ultimate sustainability.

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Michael E Bowen

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Bowen, M.E., Neuhauser, D. Understanding and managing variation: three different perspectives. Implementation Sci 8 (Suppl 1), S1 (2013). https://doi.org/10.1186/1748-5908-8-S1-S1

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The meaning of variation to healthcare managers, clinical and health-services researchers, and individual patients

Duncan neuhauser.

1 Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA

Lloyd Provost

2 Associates in Process Improvement, Austin, Texas, USA

3 Centre for Health Improvement, Chalmers University of Technology, Gothenburg, Sweden

Healthcare managers, clinical researchers and individual patients (and their physicians) manage variation differently to achieve different ends. First, managers are primarily concerned with the performance of care processes over time. Their time horizon is relatively short, and the improvements they are concerned with are pragmatic and ‘holistic.’ Their goal is to create processes that are stable and effective. The analytical techniques of statistical process control effectively reflect these concerns. Second, clinical and health-services researchers are interested in the effectiveness of care and the generalisability of findings. They seek to control variation by their study design methods. Their primary question is: ‘Does A cause B, everything else being equal?’ Consequently, randomised controlled trials and regression models are the research methods of choice. The focus of this reductionist approach is on the ‘average patient’ in the group being observed rather than the individual patient working with the individual care provider. Third, individual patients are primarily concerned with the nature and quality of their own care and clinical outcomes. They and their care providers are not primarily seeking to generalise beyond the unique individual. We propose that the gold standard for helping individual patients with chronic conditions should be longitudinal factorial design of trials with individual patients. Understanding how these three groups deal differently with variation can help appreciate these three approaches.

Introduction

Health managers, clinical researchers, and individual patients need to understand and manage variation in healthcare processes in different time frames and in different ways. In short, they ask different questions about why and how healthcare processes and outcomes change ( table 1 ). Confusing the needs of these three stakeholders results in misunderstanding.

Meaning of variation to managers, researchers and individual patients: questions, methods and time frames

Health managers

Our extensive experience in working with healthcare managers has taught us that their primary goal is to maintain and improve the quality of care processes and outcomes for groups of patients. Ongoing care and its improvement are temporal, so in their situation, learning from variation over time is essential. Data are organised over time to answer the fundamental management question: is care today as good as or better than it was in the past, and how likely is it to be better tomorrow? In answering that question, it becomes crucial to understand the difference between common-cause and special-cause variation (as will be discussed later). Common-cause variation appears as random variation in all measures from healthcare processes. 1 Special-cause variation appears as the effect of causes outside the core processes of the work. Management can reduce this variation by enabling the easy recognition of special-cause variation and by changing healthcare processes—by supporting the use of clinical practice guidelines, for example—but common-cause variation can never be eliminated.

The magnitude of common-cause variation creates the upper and lower control limits in Shewhart control charts. 2–5 Such charts summarise the work of health managers well. Figure 1 shows a Shewhart control chart (p-chart) developed by a quality-improvement team whose aim was to increase compliance with a new care protocol. The clinical records of eligible patients discharged (45–75 patients) were evaluated each week by the team, and records indicating that the complete protocol was followed were identified. The baseline control chart showed a stable process with a centre line (average performance) of 38% compliance. The team analysed the aspects of the protocol that were not followed and developed process changes to make it easier to complete these particular tasks. After successfully adapting the changes to the local environment (indicated by weekly points above the upper control limit in the ‘Implementing Changes’ period), the team formally implemented the changes in each unit. The team continued to monitor the process and eventually developed updated limits for the chart. The updated chart indicated a stable process averaging 83%.

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Annotated Shewhart control chart—using protocol.

This control chart makes it clear that a stable but inferior process was operating for the first 11 weeks and, by inference, probably before that. The annotated changes (testing, adapting and implementing new processes of care) are linked to designed tests of change which are special (assignable) causes of variation, in this case, to improvement after week 15, after which a new better stable process has taken hold. Note that there is common-cause (random) variation in both the old and improved processes.

After updating the control limits, the chart reveals a new stable process with no special-cause variation, which is to say, no points above or below the control limits (the dotted lines). Note that the change after week 15 cannot easily be explained by chance (random, or common-cause, variation), since the probability of 13 points in a row occurring by chance above the baseline control limit is one divided by 2 to the 13th power. This is the same likelihood that in flipping a coin 13 times, it will come up heads every time. This level of statistical power to exclude randomness as an explanation is not to be found in randomised controlled trials (RCTs). Although there is no hard-and-fast rule about the number of observations over time needed to demonstrate process stability and establish change, we believe a persuasive control chart requires 20–30 or more observations.

The manager's task demonstrates several important characteristics. First is the need to define the key quality characteristics, and choose among them for focused improvement efforts. The choice should be made based on the needs of patients and families. The importance of these quality characteristics to those being served means that speed in learning and improvement is important. Indeed, for the healthcare manager, information for improvement must be as rapid as possible (in real time). Year-old research data are not very helpful here; just-in-time performance data in the hands of the decision-makers provide a potent opportunity for rapid improvement. 6

Second, managerial change is holistic; that is, every element of an intervention that might help to improve and can be done is put to use, sometimes incrementally, but simultaneously if need be. Healthcare managers are actively working to promote measurement of process and clinical outcomes, take problems in organisational performance seriously, consider the root causes of those problems, encourage the formation of problem solving clinical micro-system teams and promote the use of multiple, evolving Plan–Do–Study–Act (PDSA) tests of change.

This kind of improvement reasoning can be applied to a wide range of care processes, large and small. For example, good surgery is the appropriate combination of hundreds of individual tasks, many of which could be improved in small ways. Aggregating these many smaller changes may result in important, observable improvement over time. The protocol-driven, randomised trial research approach is a powerful tool for establishing efficacy but has limitations for evaluating and improving such complex processes as surgery, which are continually and purposefully changing over time. The realities of clinical improvement call for a move from after-the-fact quality inspection to building quality measures into medical information systems, thereby creating real-time quality data for providers to act upon. Caring for populations of similar patients in similar ways (economies of scale) can be of particular value, because the resulting large numbers and process stability can help rapidly demonstrate variation in care processes 7 ; very tight control limits (minimal common-cause variation) allow special-cause variation to be detected more quickly.

Clinical and health-services researchers

While quality-management thinking tends towards the use of data plotted over time in control-chart format, clinical researchers think in terms of true experimental methods, such as RCTs. Health-services researchers, in contrast, think in terms of regression analysis as their principal tool for discovering explainable variation in processes and outcomes of care. The data that both communities of researchers use are generally collected during fixed periods of time, or combined across time periods; neither is usually concerned with the analysis of data over time.

Take, for example, the question of whether age and sex are associated with the ability to undertake early ambulation after hip surgery. Clinical researchers try to control for such variables through the use of entry criteria into a trial, and random assignment of patients to experimental or control group. The usual health-services research approach would be to use a regression model to predict the outcome (early ambulation), over hundreds of patients using age and sex as independent variables. Such research could show that age and sex predict outcomes and are statistically significant, and that perhaps 10% of the variance is explained by these two independent variables. In contrast, quality-improvement thinking is likely to conclude that 90% of the variance is unexplained and could be common-cause variation. The health-services researcher is therefore likely to conclude that if we measured more variables, we could explain more of this variance, while improvement scientists are more likely to conclude that this unexplained variance is a reflection of common-cause variation in a good process that is under control.

The entry criteria into RCTs are carefully defined, which makes it a challenge to generalise the results beyond the kinds of patients included in such studies. Restricted patient entry criteria are imposed to reduce variation in outcomes unrelated to the experimental intervention. RCTs focus on the difference between point estimates of outcomes for entire groups (control and experimental), using statistical tests of significance to show that differences between the two arms of a trial are not likely to be due to chance.

Individual patients and their healthcare providers

The question an individual patient asks is different from those asked by manager and researcher, namely ‘How can I get better?’ The answer is unique to each patient; the question does not focus on generalising results beyond this person. At the same time, the question the patient's physician is asking is whether the group results from the best clinical trials will apply in this patient's case. This question calls for a different inferential approach. 8–10 The cost of projecting general findings to individual patients could be substantial, as described below.

Consider the implications of a drug trial in which 100 patients taking a new drug and 100 patients taking a placebo are reported as successful because 25 drug takers improved compared with 10 controls. This difference is shown as not likely to be due to chance. (The drug company undertakes a multimillion dollar advertising campaign to promote this breakthrough.) However, on closer examination, the meaning of these results for individual patients is not so clear. To begin with, 75 of the patients who took the drug did not benefit. And among those 25 who benefited, some, perhaps 15, responded extremely well, while the size of the benefit in the other 10 was much smaller. To have only the 15 ‘maximum responders’ take this drug instead of all 100 could save the healthcare system 85% of the drug's costs (as well as reduce the chance of unnecessary adverse drug effects); those ‘savings’ would, of course, also reduce the drug company's sales proportionally. These considerations make it clear that looking at more than group results could potentially make an enormous difference in the value of research studies, particularly from the point of view of individual patients and their providers.

In light of the above concerns, we propose that the longitudinal factorial study design should be the gold standard of evidence for efficacy, particularly for assessing whether interventions whose efficacy has been established through controlled trials are effective in individual patients for whom they might be appropriate ( box 1 ). Take the case of a patient with hypertension who measures her blood pressure at least twice every day and plots these numbers on a run chart. Through this informal observation, she has learnt about several factors that result in the variation in her blood pressure readings: time of day, the three different hypertension medicines she takes (not always regularly), her stress level, eating salty French fries, exercise, meditation (and, in her case, saying the rosary), and whether she slept well the night before. Some of these factors she can control; some are out of her control.

Longitudinal factorial design of experiments for individual patients

The six individual components of this approach are not new, but in combination they are new 8 9

  • One patient with a chronic health condition; sometimes referred to as an ‘N-of-1 trial.’
  • Care processes and health status are measured over time. These could include daily measures over 20 or more days, with the patient day as the unit of analysis.
  • Whenever possible, data are numerical rather than simple clinical observation and classification.
  • The patient is directly involved in making therapeutic changes and collecting data.
  • Two or more inputs (factors) are experimentally and concurrently changed in a predetermined fashion.
  • Therapeutic inputs are added or deleted in a predetermined, systematic way. For example: on day 1, drug A is taken; on day 2, drug B; on day 3, drug A and B; day 4, neither. For the next 4 days, this sequence could be randomly reordered.

Since she is accustomed to monitoring her blood pressure over time, she is in an excellent position to carry out an experiment that would help her optimise the effects of these various influences on her hypertension. Working with her primary care provider, she could, for example, set up a table of randomly chosen dates to make each of several of these changes each day, thereby creating a systematically predetermined mix of these controllable factors over time. This factorial design allows her to measure the effects of individual inputs on her blood pressure, and even interactions among them. After an appropriate number of days (perhaps 30 days, depending on the trade-off between urgency and statistical power), she might conclude that one of her three medications has no effect on her hypertension, and she can stop using it. She might also find that the combination of exercise and consistently low salt intake is as effective as either of the other two drugs. Her answers could well be unique to her. Planned experimental interventions involving single patients are known as ‘N-of-1’ trials, and hundreds have been reported. 10 Although longitudinal factorial design of experiments has long been used in quality engineering, as of 2005 there appears to have been only one published example of its use for an individual patient. 8 9 This method of investigation could potentially become widely used in the future to establish the efficacy of specific drugs for individual patients, 11 and perhaps even required, particularly for very expensive drug therapies for chronic conditions. Such individual trial results could be combined to obtain generalised knowledge.

This method can be used to show (1) the independent effect of each input on the outcome, (2) the interaction effect between the inputs (perhaps neither drug A or B is effective on its own, but in combination they work well), (3) the effect of different drug dosages and (4) the lag time between treatment and outcome. This approach will not be practical if the outcome of interest occurs years later. This method will be more practical with patient access to their medical record where they could monitor all five of Bergman's core health processes. 12

Understanding variation is one of the cornerstones of the science of improvement

This broad understanding of variation, which is based on the work of Walter Shewart in the 1920s, goes well beyond such simple issues as making an intended departure from a guideline or recognising a meaningful change in the outcome of care. It encompasses more than good or bad variation (meeting a target). It is concerned with more than the variation found by researchers in random samples from large populations.

Everything we observe or measure varies. Some variation in healthcare is desirable, even essential, since each patient is different and should be cared for uniquely. New and better treatments, and improvements in care processes result in beneficial variation. Special-cause variation should lead to learning. The ‘Plan–Do–Study’ portion of the Shewhart PDSA cycle can promote valuable change.

The ‘act’ step in the PDSA cycle represents the arrival of stability after a successful improvement has been made. Reducing unintended, and particularly harmful, variation is therefore a key improvement strategy. The more variation is controlled, the easier it is to detect changes that are not explained by chance. Stated differently, narrow limits on a Shewhart control chart make it easier and quicker to detect, and therefore respond to, special-cause variation.

The goal of statistical thinking in quality improvement is to make the available statistical tools as simple and useful as possible in meeting the primary goal, which is not mathematical correctness, but improvement in both the processes and outcomes of care. It is not fruitful to ask whether statistical process control, RCTs, regression equations or longitudinal factorial design of experiments is best in some absolute sense. Each is appropriate for answering different questions.

Forces driving this new way of thinking

The idea of reducing unwanted variation in healthcare represents a major shift in thinking, and it will take time to be accepted. Forces for this change include the computerisation of medical records leading to public reporting of care and outcome comparisons between providers and around the world. This in turn will promote pay for performance, and preferred provider contracting based on guideline use and good outcomes. This way of thinking about variation could spread across all five core systems of health, 12 including self-care and processes of healthy living.

Competing interests: None.

Provenance and peer review: Not commissioned; externally peer reviewed.

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Common Cause vs. Special Cause Variation

By OpEx Learning Team , Last Updated January 23, 2018

special cause variation vs common

Variation is something that Six Sigma tries to remedy as much as possible, as it tends to be harmful to most businesses’ operations. Of course, there are some types of variation that are unavoidable, and in some cases even desirable, but for the most part, it’s something you’ll want to keep out of your regular reports. However, some leaders don’t understand the intricate differences between the most general types of variation common cause and special cause variation. Knowing how they differ and how both can be related to your operations can be a huge benefit in preventing variation in general.

Common Cause Variation

Common cause variation is generally seen as something that’s inherent to the way a specific process runs, and occurs from natural sources. This may also make it unavoidable in some cases, although it doesn’t automatically imply that, so the two states should not be confused. The important point is that common cause variation is usually something you can account for and work to avoid preemptively, without even running a single iteration of the actual process.

It’s useful to gather as much data as you can about the common cause variation relevant to your business, as you’re going to want to put special effort into preventing it in your operations early on. Studying this part of your operations carefully can have huge benefits later on, and it can effectively help you avoid huge amounts of waste in your regular operations.

Special Cause Variation

On the contrary, special cause variation is variation that’s caused by unpredictable factors special cases that tend to be unique. As you’re probably guessing, there are no reliable mechanisms in place for avoiding special cause variation, and it’s something you’ll just have to deal with in most cases. Of course, it’s possible to put up preventive measures to work around it to some extent, but you’ll want to maintain a good balance between that and the ability of your workers to actually do their job.

Because, as it often turns out, minimizing special cause variation often requires you to introduce additional steps in your processes, slowing them down and potentially annoying some of your workers. It’s pretty much unavoidable to make people somewhat displeased when you’re trying to avoid variation, but on the other hand, you should do it in a considerate way that does not unnecessarily slow down your entire operation. That’s what most leaders tend to get wrong, and it’s the main reason you should take the time to study the intricacies of special cause variation in your business as much as possible.

How Much Should I Focus on Both?

Generally speaking, preventing common cause variation is usually a better use of your efforts if you’re going to invest a lot of time into that part of your operations. As we mentioned above, special cause variation can be difficult to predict, and also quite challenging to prevent without impacting the pace of your operations. Of course, that’s not a universal rule, and if you can figure out a way to get it done without making things too difficult for everyone, then by all means have a go at it.

But make sure you do that in a coordinated way that’s aligned with the interests of your workers, and you get enough input from them at every step of the process. Otherwise, you might end up in a situation where you’ve made significant changes to your business but they are actually making things worse, and there’s no viable exit in sight.

Understanding the differences between common cause and special cause variation is important if you want to deal with variation in your operations in general. Knowing which of the two is more common for your own operations, and what you can do to prevent this kind of variation, can help you a lot when you want to make sure that things are running smoothly and without any unnecessary interruptions.

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Using control charts to detect common-cause variation and special-cause variation

In this topic, what are common-cause variation and special-cause variation, what special-cause variation looks like on a control chart, using brainstorming to investigate special-cause variation, don't overcorrect your process for common-cause variation.

Some degree of variation will naturally occur in any process. Common-cause variation is the natural or expected variation in a process. Special-cause variation is unexpected variation that results from unusual occurrences. It is important to identify and try to eliminate special-cause variation. Out-of-control points and nonrandom patterns on a control chart indicate the presence of special-cause variation.

Examples of common-cause and special-cause variation

A process is stable if it does not contain any special-cause variation; only common-cause variation is present. Control charts and run charts provide good illustrations of process stability or instability. A process must be stable before its capability is assessed or improvements are initiated.

special cause variation vs common

This process is stable because the data appear to be distributed randomly and do not violate any of the 8 control chart tests.

special cause variation vs common

This process is not stable; several of the control chart tests are violated.

A good starting point in investigating special-cause variation is to gather several process experts together. Using the control chart, encourage the process operators, the process engineers, and the quality testers to brainstorm why particular samples were out of control. Depending on your process, you may also want to include the suppliers in this meeting.

  • Which samples were out of control?
  • Which tests for special causes did the samples fail?
  • What does each failed test mean?
  • What are all the possible reasons for the failed test?

A common method for brainstorming is to ask questions about why a particular failure occurred to determine the root cause (the 5 why method). You could also use a cause-and-effect diagram (also called fishbone diagram).

While it's important to avoid special-cause variation, trying to eliminate common-cause variation can make matters worse. Consider a bread baking process. Slight drifts in temperature that are caused by the oven's thermostat are part of the natural common-cause variation for the process. If you try to reduce this natural process variation by manually adjusting the temperature setting up and down, you will probably increase variability rather than decrease it. This is called overcorrection.

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Common Cause Variation vs Special Cause Variation

Common Cause Variation vs Special Cause Variation

What are the causes of variation in quality control or what are the types of variation? The term “variation” is widely used in statistics, quality management, genetics, and even in biology. It refers to the measurement for a group of numbers that spread out from their average value. Every measured data set involves some degree of variation even if the degree is slight. It is a numerical value specifies how widely data in a data set vary. A small variance shows that data are closer to the average and a high variance shows that data are very different than each other. There are two causes of variation in quality control which are Common Cause Variation and Special Cause Variation. In this article, we will give examples of common cause of variance and special cause of variance for the control chart.

Table of Contents

Types of Variation

Classification of variance is very important in project quality management. Common cause of variance and special cause of variance have different origins. In order to take action to improve your process or prevent future problems related to variations, you must know the type of variation that will affect your processes. Walter A. Shewhart developed control charts to distinguish both variations in the 1920s. He discovered that all the processes involve common cause variation but some processes which are not in control show special cause variation.

Common Cause Variation

According to the studies of Walter A. Shewhart and W. Edwards Deming common causes can be called natural patterns. Common cause of variances are quantifiable, expected, natural, usual, historical and random causes of variances in a process. Common cause variation shows the process potential when the special causes of variance is eliminated from the system. On a control chart , common cause variation indicates a random distribution around the control limit (or average limit).

It is possible to predict them probabilistically but specific actions cannot be taken to prevent the occurrence of common cause of variances. Therefore a management action is required to make an extensive change on the system to reduce the amount of common cause of variances.

Simply put, common cause of variances are normal, consistent and inherent in the process which can not be eliminated.

Note that if a process containing only  common cause variation is said to be in statistical control.

Examples for Common Cause Variation

Assume that in a hotel construction project, you estimated 10 days to complete a formwork activity. Due to the climatic conditions, it is completed in 11 days. The completion time has not deviated too much from the mean. This is an example of a common cause variation.

Another example is that your team is working on a software development project. Due to the lack of coordination between team members, unclear scope definition and unexpected errors, you will not complete the project on time.

Below can be some examples of common cause of variance within a project

  • Unclear scope definition
  • Inadequate design
  • Poor management
  • Insufficient procedures
  • Weather conditions
  • Temperature
  • Computer response time
  • Inadequate working conditions

Special Cause Variation

Unlike the common cause of variance, special cause of variance refers to known factors that have effects on a process. W. Edwards Deming introduced this concept. Special cause of variance are the unusual, non-quantifiable, unexpected variances that were not encountered before in a process. Mostly a specific factor such as a rapid change in conditions or input parameters causes special cause variations.

On a control chart special causes of variance indicates a non-random distribution around the control limit (or average limit).

Special causes of variance can usually be eliminated with adjustments to the processes, components or methods. They may cause serious problems if they are not eliminated. Special causes of variances are not inherent and usually originate from technical problems.

Simply put, special cause of variance are caused by unpredictable factors that can not be foreseen with the help of historical experience and records.

After analyzing an example for common cause variation, we will analyze an example of special cause variation.

Examples for Special Cause Variation

Assume that you are a project manager of a bridge construction project and you estimated 10 days to complete an excavation activity. When you started excavation, a technical problem occurred in the hydraulic system of the excavator.  This malfunction delayed this activity for about 20 days. The problem is solved by fixing up the hydraulic system. This is an example of a special cause variation.

Another example is that you were working with a shipping company to transport a generator for a hospital renovation project. Estimated time of arrival was 2 days. But the arrival of the generator took four days because of an accident on the highway.

Below can be some examples for special causes of variances within a project

• Machine fault. • Power surges • Operator absent/ falls asleep • Computer fault.

While analyzing a data set, we see that all the data are not close to each other. There may be some great and small differences between them. The variance shows how data are distributed around an expected or an average value. If the degree of variance is close or equal to zero this means that all the data are the same or very close to each other. A high degree of variance indicates that all the data are far away from each other.

Common and special causes are the two distinct origins of variation in a system. In a process, it is important to determine the type of variance because the course of action you take will depend on the types of variation. Using a control chart helps to distinguish Common Cause of Variance and Special Cause of Variance.

In this article, talked about the causes of variation in quality control and make a review for Common Cause Variation and Special Cause Variation. Note that this is an important subject tested on the CAPM and PMP Certification exams. We hope that it will be useful for passing PMP and CAPM exams.

Control Chart versus Run Chart

special cause variation vs common

Since 2004 I work for ICT Management which provides worldwide quality management service. Passionate about new technologies, i have the privilege to implement many new systems and applications for different departements of my company. I have Six Sigma Green Belt.

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  • Largest Covid Vaccine Study Yet Finds Links to Health Conditions

(Bloomberg) -- Vaccines that protect against severe illness, death and lingering long Covid symptoms from a coronavirus infection were linked to small increases in neurological, blood, and heart-related conditions in the largest global vaccine safety study to date.

The rare events — identified early in the pandemic — included a higher risk of heart-related inflammation from mRNA shots made by Pfizer Inc., BioNTech SE, and Moderna Inc., and an increased risk of a type of blood clot in the brain after immunization with viral-vector vaccines such as the one developed by the University of Oxford and made by AstraZeneca Plc. 

The viral-vector jabs were also tied to an increased risk of Guillain-Barre syndrome , a neurological disorder in which the immune system mistakenly attacks the peripheral nervous system.

More than 13.5 billion doses of Covid vaccines have been administered globally over the past three years, saving over 1 million lives in Europe alone. Still, a small proportion of people immunized were injured by the shots, stoking debate about their benefits versus harms.

The new research, by the Global Vaccine Data Network, was published in the journal Vaccine last week, with the data made available via interactive dashboards to show methodology and specific findings. 

Read More: Covid Test Failures Highlight Evolving Relationship With Virus

The research looked for 13 medical conditions that the group considered “adverse events of special interest” among 99 million vaccinated individuals in eight countries, aiming to identify higher-than-expected cases after a Covid shot. The use of aggregated data increased the possibility of identifying rare safety signals that might have been missed when looking only at smaller populations.

Myocarditis , or inflammation of the heart muscle, was consistently identified following a first, second and third dose of mRNA vaccines, the study found. The highest increase in the observed-to-expected ratio was seen after a second jab with the Moderna shot. A first and fourth dose of the same vaccine was also tied to an increase in pericarditis, or inflammation of the thin sac covering the heart. 

Safety Signals

Researchers found a statistically significant increase in cases of Guillain-Barre syndrome within 42 days of an initial Oxford-developed ChAdOx1 or “Vaxzevria” shot that wasn’t observed with mRNA vaccines. Based on the background incidence of the condition, 66 cases were expected — but 190 events were observed. 

ChAdOx1 was linked to a threefold increase in cerebral venous sinus thrombosis, a type of blood clot in the brain, identified in 69 events, compared with an expected 21. The small risk led to the vaccine’s withdrawal or restriction in Denmark and multiple other countries. Myocarditis was also linked to a third dose of ChAdOx1 in some, but not all, populations studied.

Possible safety signals for transverse myelitis — spinal cord inflammation — after viral-vector vaccines were identified in the study. So was acute disseminated encephalomyelitis — inflammation and swelling in the brain and spinal cord — after both viral-vector and mRNA vaccines. 

Listen to the  Big Take  podcast on  iHeart ,  Apple Podcasts ,  Spotify  and the Bloomberg Terminal.  Read the transcript .

Seven cases of acute disseminated encephalomyelitis after vaccination with the Pfizer-BioNTech vaccine were observed, versus an expectation of two.  

The adverse events of special interest were selected based on pre-established associations with immunization, what was already known about immune-related conditions and pre-clinical research. The study didn’t monitor for postural orthostatic tachycardia syndrome , or POTS, that some research has linked with Covid vaccines.

Exercise intolerance, excessive fatigue, numbness and “brain fog” were among common symptoms identified in more than 240 adults experiencing chronic post-vaccination syndrome in a separate study conducted by the Yale School of Medicine. The cause of the syndrome isn’t yet known, and it has no diagnostic tests or proven remedies.

Read More: Strenuous Exercise May Harm Long Covid Sufferers, Study Shows

The Yale research aims to understand the condition to relieve the suffering of those affected and improve the safety of vaccines, said Harlan Krumholz, a principal investigator of the study, and director of the Yale New Haven Hospital Center for Outcomes Research and Evaluation. 

Read this next :  Why Driving a Few Miles Can Save Patients a Fortune on Health Care

“Both things can be true,” Krumholz said in an interview. “They can save millions of lives, and there can be a small number of people who’ve been adversely affected.” 

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A healthcare worker administers a dose of the Novavax Covid-19 vaccine at a pharmacy in Schwenksville, Pennsylvania, US, on Monday, Aug. 1, 2022. Novavax's protein-based Covid-19 vaccine received long-sought US emergency-use authorization in July, but use is likely to be limited.

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hochul gestures while speaking at podium

New York governor seeks to quell business owners’ fears after Trump ruling

Kathy Hochul says law-abiding businesspeople have ‘nothing to worry about’ after question on state’s commercial climate

The New York governor has told business owners in her state that there is “nothing to worry about” after Donald Trump was fined $355m and temporarily banned from engaging in commerce in the state when he lost his civil fraud trial on Friday.

In an interview on the New York radio show the Cats Roundtable with the supermarket billionaire John Catsimatidis, Kathy Hochul sought to quell fears in some quarters that the penalties handed to Trump for engaging in fraudulent business practices could chill the state’s commercial climate.

Asked if businesspeople should be worried that if prosecutors could “do that to the former president, they can do that to anybody”, Hochul said: “Law-abiding and rule-following New Yorkers who are businesspeople have nothing to worry about because they’re very different than Donald Trump and his behavior.”

She added that the fraud case against Trump resulted from “really an extraordinary, unusual circumstance”.

Hochul’s comments were directed at some New York business leaders who said they were concerned that the attorney general Letitia James ’s case against Trump could deter businesses and investment from coming to the state. Hochul noted James’s case demonstrated how Trump and some allies obtained favorable bank loans and insurance rates with inflated real estate values.

The governor said most New York business owners were “honest people, and they’re not trying to hide their assets and they’re following the rules”.

Hochul said most business owners would not merit state intervention.

“This judge determined that Donald Trump did not follow the rules,” Hochul added. “He was prosecuted and truly, the governor of the state of New York does not have a say in the size of a fine, and we want to make sure that we don’t have that level of interference.”

Trump, who denied wrongdoing in the case and maintained there were no victims, now has 30 days to come up with a non-recoverable $35m to secure a bond – a third-party guarantee – against his real estate holdings to show that he can pay the full fine if his appeals fail.

Alternatively, he could put the $355m into an escrow account but would get the money back if he wins on appeal.

Either way, the ruling is a blow to the developer-politician whose sense of self is tied to financial success. And James has said Trump is actually in line to pay more than $463m when interest is taken into account.

In September, Trump’s former lawyer Christopher Kise argued in court that the decision against the ex-president would cause “irreparable impact on numerous companies”. It would also threaten 1,000 employees within the Trump empire, Kise maintained.

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But the judge, Arthur Engoron, who found the former president liable for fraud and assessed the fine and three-year disqualification from doing business in New York, dropped an earlier ruling to dissolve all the companies that Trump owns in the state that could have led to a liquidation.

“This is a venial sin, not a mortal sin,” Engoron wrote in a 92-page ruling that allowed the Trump businesses to keep operating and appointed two overseers to monitor “major activities that could lead to fraud”.

Engoron said he could renew his call for “restructuring and potential dissolution” based on “substantial evidence”.

Trump has lashed out at the ruling, vowing to appeal and calling James and Engoron “corrupt”.

But James said on Friday: “This long-running fraud was intentional, egregious, illegal.” She added: “There cannot be different rules for different people in this country, and former presidents are no exception.”

This article was amended on 18 February 2024 to correct a misspelling. An earlier version referred to “venal” rather than “venial” sin.

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special cause variation vs common

Understanding Common Cause Variation: Managing Process Fluctuation

Updated: December 17, 2023 by Ken Feldman

special cause variation vs common

Variation is a given for every process. Distinguishing between the variation caused by the process inputs and that of special causes is important to understand so you can manage the variation.  

Overview: What is common cause? 

Common cause, in the context of variation, is a term coined by famous statistician Dr. W. Edwards Deming to describe the natural fluctuation in your process caused by the inputs or elements of the process. 

The typical inputs to a process are people, methods, materials, environment and equipment. The combined variation from these individual sources will produce predictable and random variation in the output of the process. This steady state variation is the best you can expect given the variation of the input factors.

Since common cause variation is inherent in your process, any attempt to reduce or eliminate it will require action be taken on the source(s) of the individual input or factor variation. This will require statistical and root cause analyses to determine the source as well as how to reduce or eliminate it. Tools such as the control chart , multi-vari chart , fishbone diagram and hypothesis testing are useful for identifying the possible source of the common cause variation.

An industry example of common cause 

It is not uncommon for organizations to misuse a control chart when trying to react to common cause variation. The control chart below shows an in-control process exhibiting common cause variation. All points are within the control limits and appear to be random.

special cause variation vs common

Unfortunately, the supervisor of the department did not understand how to properly interpret and use a control chart. He praised the team for performing well at point 6 (higher is better) and angrily wanted to know what happened at point 10. The fact is, nothing happened. This would be analogous to asking why a pair of dice threw a 3 or an 11. You would expect 3s and 11s from a fair pair of dice. 

Since the variation is common cause, there is no simple answer. Asking what happened will only send people on a fruitless and frustrating mission to find an excuse rather than a reason. The supervisor must identify the specific source of the variation and then make a fundamental change in the process to alter the variation. 

Frequently Asked Questions (FAQ) about common cause

How do you determine whether your process is exhibiting common cause variation .

The best tool to use is the control chart. Depending on the type of data you have, you will select the appropriate type of control chart to use. If the process is exhibiting common cause variation, the pattern on the control chart will have all the data points within the upper control limit and lower control limit and show a random pattern over time.

How can you reduce common cause variation? 

Common cause variation is a result of the combined variation of the people, methods, materials, equipment and environment of your process. You must drill down and identify which of the potential sources of this inherent variation is the primary cause of the variation. By changing that factor, you will reduce the common cause variation.

Is all common cause variation good? 

There is no value judgment of “good” or “bad” associated with common cause variation. You might have common cause variation in a process that consistently produces defective products. Saying it is common cause variation only means it is steady state and predictable, not good or bad. 

About the Author

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Ken Feldman

IMAGES

  1. What is Six Sigma Process Variation

    special cause variation vs common

  2. Common Cause Vs Special Cause Variation [ VARIATIONS ]

    special cause variation vs common

  3. Special Cause vs. Common Cause Variation

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  4. Common Cause Variations Vs Special Cause Variations

    special cause variation vs common

  5. What is Meant by Variation in Processes?

    special cause variation vs common

  6. Common Cause Variation vs Special Cause Variation

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VIDEO

  1. Common Cause Variation

  2. Direct Variation

  3. Meaning and Relevance of Statistics

  4. 24 Generalization & Specialization

  5. Common Cause Variation vs. Special Cause Variation

  6. One common cause for poor skin health & hair health

COMMENTS

  1. Common Cause vs. Special Cause Variation: What's the Difference?

    In short, common cause variations reflect a stable process, while special cause variations reflect an unstable process. Common Cause vs. Special Cause: Who would use A and/or B? Both of these types of variation are important to have an understanding of in project management.

  2. Common cause and special cause (statistics)

    Briefly, "common causes", also called natural patterns, are the usual, historical, quantifiable variation in a system, while "special causes" are unusual, not previously observed, non-quantifiable variation.

  3. Common Cause Variation Vs. Special Cause Variation

    Types of Variance Change is inevitable, even in statistics. You'll need to know what kind of variation affects your process because the course of action you take will depend on the type of variance. There are two types of Variance: Common Cause Variation and Special Cause Variation.

  4. Special Cause vs. Common Cause Variation

    $135 $16 Common Cause Vs Special Cause: Types of Variation Common cause variation is the natural variation in the process. It is a part of the process. There are "many" causes of this type of variation, and it is not easy to identify and remove these. You will need to live with them unless drastic action is taken, such as process re-engineering.

  5. Common Cause & Special Cause Variation Explained with Examples

    Common cause variation refers to the natural and measurable anomalies that occur in the system or business processes. It naturally exists within the system. While it is true that variance may bring a negative impact to business operations, we cannot escape from this aspect. It is inherent and will always be.

  6. The Power of Special Cause Variation: Learning from Process Changes

    A special cause of variation is assignable to a defect, fault, mistake, delay, breakdown, accident, and/or shortage in the process. When special causes are present, process quality is unpredictable. Special causes are a signal for you to act to make the process improvements necessary to bring the process measure back into control.

  7. Improvement Science in Healthcare Yields True Change

    Common Cause Variation vs. Special Cause Variation. To apply improvement science, understand clinical variation, and take the right measures to overcome this unwarranted variation, healthcare leaders must understand the two types of variation. The first is common cause variation and the second is special cause variation.

  8. Know It When You See It: Identifying and Using Special Cause Variation

    In contrast, special cause variations are causes of variations that are not inherent to the system. 4 Although there are different rules that signify special cause variation in SPC charts, some of the most common rules that we will focus on here include (1) a single data point outside of the control limits, (2) 8 consecutive points above or belo...

  9. Understanding and managing variation: three different perspectives

    Special-cause variation is an unpredictable deviation resulting from a cause that is not an intrinsic part of a process. By careful and systematic measurement, it is easier to detect changes that are not random variation.

  10. The meaning of variation to healthcare managers, clinical and health

    In answering that question, it becomes crucial to understand the difference between common-cause and special-cause variation (as will be discussed later). Common-cause variation appears as random variation in all measures from healthcare processes. 1 Special-cause variation appears as the effect of causes outside the core processes of the work ...

  11. Common Cause vs. Special Cause: The Basics of Process Variation

    Common Cause vs. Special Cause: The Basics of Process Variation Home » Business » Common Cause vs. Special Cause: The Basics of Process Variation By OpEx Learning Team, Last Updated April 26, 2018

  12. Knowledge of Variation

    Dr. Deming provided the means for management to do just that through knowledge of variation. In any business, there are always variations, between people, in output, in service and in product. The output of a system results from two types of variation: common cause and special cause variation.

  13. What is Variation?

    The Law of Variation is defined as the difference between an ideal and an actual situation. Variation or variability is most often encountered as a change in data, expected outcomes, or slight changes in production quality. Variation usually occurs in four separate areas: Special causes. Common causes.

  14. Common-Cause vs. Special-Cause

    Six Sigma Terms Common-Cause vs. Special-Cause Common-cause variation is where no one, or combination of factors is unduly affected the process variation (random variation). Special-cause variation is when one or more factors are affecting the process variation in a non-random way.

  15. Distinguishing between common cause variation and special cause

    Believing that special cause variation is common cause variation can lead decision makers to ignore a specific cause that degrades system performance. Understanding the differences between the two types of variation can lead to more effective processes and higher-quality production systems. The methods proposed in this research can also be ...

  16. Achieving Process Stability with Common Cause Variation ...

    RELATED: COMMON CAUSE VARIATION VS. SPECIAL CAUSE VARIATION 3 benefits of common cause variation . When your process consists of common cause variation, it is time to "Shrink, shrink, shrink that variation!" — W. Edwards Deming . When the process is in statistical control: 1. The process outcome is predictable in the short term. This is a ...

  17. Common Cause vs. Special Cause Variation

    On the contrary, special cause variation is variation that's caused by unpredictable factors special cases that tend to be unique. As you're probably guessing, there are no reliable mechanisms in place for avoiding special cause variation, and it's something you'll just have to deal with in most cases.

  18. Using control charts to detect common-cause variation and special-cause

    Some degree of variation will naturally occur in any process. Common-cause variation is the natural or expected variation in a process. Special-cause variation is unexpected variation that results from unusual occurrences. It is important to identify and try to eliminate special-cause variation. Out-of-control points and nonrandom patterns on a ...

  19. Statistics Tip : Common Cause vs. Special Cause Variation

    Variation which is not Common Cause is called Special Cause Variation. It is a signal that factors outside the process are affecting it. Any Special Cause Variation must be eliminated before one can attempt to narrow the range of Common Cause Variation. Until we eliminate Special Cause Variation, we don't have a process that we can improve.

  20. Common Cause Variation vs Special Cause Variation

    Common cause of variances are quantifiable, expected, natural, usual, historical and random causes of variances in a process. Common cause variation shows the process potential when the special causes of variance is eliminated from the system.

  21. Identifying and Managing Special Cause Variations

    Common cause accounts for closer to 90% of variances that may occur. Eliminating special cause variations. While it can be difficult to predict an initial special cause variation, steps can be taken to help ensure that the same problem does not arise again. Technical improvements, proper training, and other strategies can all help protect your ...

  22. Distinguishing between Common Cause Variation and Special Cause

    Confusing common cause and special cause variation can lead to incorrect decisions. This article analyzes the impact of an individual's ability to distinguish between common cause and special cause variation by simulating a manufacturing system with several human operators and a production manager. We use a recognition primed

  23. Largest Covid Vaccine Study Yet Finds Links to Health Conditions

    Sponsored Content. Vaccines that protect against severe illness, death and lingering long Covid symptoms from a coronavirus infection were linked to small increases in neurological, blood, and ...

  24. New York governor seeks to quell business owners' fears after Trump

    The New York governor has told business owners in her state that there is "nothing to worry about" after Donald Trump was fined $355m and temporarily banned from engaging in commerce in the ...

  25. Understanding Common Cause Variation: Managing Process ...

    Common cause, in the context of variation, is a term coined by famous statistician Dr. W. Edwards Deming to describe the natural fluctuation in your process caused by the inputs or elements of the process. The typical inputs to a process are people, methods, materials, environment and equipment. The combined variation from these individual ...