Common-cause and special-cause 435399 210767968 2008-05-07T09:23:06Z Melcombe 4682566 revised categories {| class="wikitable" align=right ! Type of variation !! Synonyms |- | Common cause || Chance cause<BR>Non-assignable cause<BR>Noise<BR>Natural pattern |- | Special cause || Assignable cause<BR>Signal<BR>Unnatural pattern |} '''Common- and special-causes''' are the two distinct origins of variation, in a [[Process (general)|process]] that features in the [[statistics|statistical]] thinking and methods of [[Walter A. Shewhart]] and [[W. Edwards Deming]]. However, it can be argued that they were recognised and discussed as early as [[1703]] by [[Gottfried Leibniz]] and are particularly important in the thinking of [[economist]]s [[Frank Knight]], [[John Maynard Keynes]] and [[G. L. S. Shackle]]. Several alternative names have been used over the years. ==Origins and concepts== In [[1703]], [[Jacob Bernoulli]] wrote to [[Gottfried Leibniz]] to discuss their shared interest in applying [[mathematics]] and [[probability]] to games of chance. [[Jacob Bernoulli|Bernoulli]] speculated whether it would be possible to gather [[Mortality rate|mortality]] data from gravestones and thereby calculate, by their existing practice, the [[probability]] of a man currently aged 20 years outliving a man aged 60 years. [[Gottfried Leibniz|Leibniz]] replied that he doubted this was possible as: ''Nature has established patterns originating in the return of events but only for the most part. New illnesses flood the human race, so that no matter how many experiments you have done on corpses, you have not thereby imposed a limit on the nature of events so that in the future they could not vary.'' This captures the central idea that some variation is predictable, at least approximately in frequency. This ''common-cause variation'' is evident from the experience base. However, new, unanticipated, emergent or previously neglected phenomena (''e.g.'' "new diseases") result in variation outside the historical experience base. [[Walter A. Shewhart|Shewhart]] and [[W. Edwards Deming|Deming]] argued that such ''special-cause variation'' is fundamentally unpredictable in frequency of occurrence or in severity. [[John Maynard Keynes]] emphasised the importance of special-cause variation when he wrote: ''By “uncertain” knowledge … I do not mean merely to distinguish what is known for certain from what is only probable. The game of roulette is not subject, in this sense, to uncertainty ... The sense in which I am using the term is that in which the prospect of a European war is uncertain, or the price of copper and the rate of interest twenty years hence, or the obsolescence of a new invention … About these matters there is no scientific basis on which to form any calculable probability whatever. We simply do not know!'' ==Definitions== ===Common-cause variation=== Common-cause variation is characterised by: *Phenomena constantly active within the system; *Variation predictable [[probability|probabilistically]]; *Irregular variation within an historical experience base; and *Lack of significance in individual high or low values. The outcomes of a [[roulette]] wheel are a good example of common-cause variation. Common-cause variation is the ''noise'' within the system. [[Walter A. Shewhart]] originally used the term ''chance-cause''.<REF>{{Citation | last = Shewhart | first = Walter A. | author-link = Walter A. Shewhart | publication-date = 1931 | title = Economic control of quality of manufactured product | publication-place = [[New York, New York]] | publisher = D. Van Nostrand Company, Inc | oclc = 1045408 | page = 7 }}</REF> The term ''common-cause'' was coined by [[Harry Alpert]] in [[1947]]. The [[Western Electric Company]] used the term ''natural pattern''.<REF>{{Citation | last = [[Western Electric Company]] | publication-date = 1956 | title = Introduction to [[Statistical Quality Control]] handbook. | edition = 1 | publication-place = [[Indianapolis, Indiana]] | publisher = Western Electric Co. | oclc = 33858387 | page = 23-24 }}</REF> [[Walter A. Shewhart|Shewhart]] called a process that features only common-cause variation as being ''in [[statistical control]]''. This term is deprecated by some modern statisticians who prefer the phrase ''stable and predictable''. ===Special-cause variation=== Special-cause variation is characterised by: *New, unanticipated, emergent or previously neglected phenomena within the system; *Variation inherently unpredictable, even [[probability|probabilistically]]; *Variation outside the historical experience base; and *Evidence of some inherent change in the system or our knowledge of it. Special-cause variation always arrives as a surprise. It is the ''signal'' within a system. [[Walter A. Shewhart]] originally used the term ''assignable-cause''.<REF>{{Citation | last = Shewhart | first = Walter A. | author-link = Walter A. Shewhart | publication-date = 1931 | title = Economic control of quality of manufactured product | publication-place = [[New York, New York]] | publisher = D. Van Nostrand Company, Inc | oclc = 1045408 | page = 14 }}</REF> The term ''special-cause'' was coined by [[W. Edwards Deming]]. The [[Western Electric Company]] used the term ''unnatural pattern''.<REF>{{Citation | last = [[Western Electric Company]] | publication-date = 1956 | title = Introduction to [[Statistical Quality Control]] handbook. | edition = 1 | publication-place = [[Indianapolis, Indiana]] | publisher = Western Electric Co. | oclc = 33858387 | page = 23-24 }}</REF> ==Examples== ===Special causes=== * Poor adjustment of equipment * Operator falls asleep * Faulty controllers * Machine malfunction * Computer crashes * Poor batch of raw material * Power surges * High healthcare demand from elderly people * Abnormal traffic (click-fraud) on web ads<ref name="frinhealth">{{cite web | title = Financial Risk in Healthcare Provision and Contracts | url = http://www.decisioneering.com/cbuc/2004/papers/CBUC04-Jones.pdf | accessmonthday=13 November |accessyear = 2006 }}</ref> * Extremely long lab testing turnover time due to switching to a new computer system * Operator absent<ref name="siscc">{{cite web | title = Statistical Inference | url = http://www.anu.edu.au/nceph/surfstat/surfstat-home/5-1-2.html | accessmonthday=13 November |accessyear = 2006 }}</ref> === Common causes === * Inappropriate procedures * Poor design * Poor maintenance of machines * Lack of clearly defined [[standing operating procedure]]s * Poor working conditions, e.g. lighting, noise, dirt, temperature, ventilation * Machines not suited to the job * Substandard raw materials * Assurement error * [[Quality control]] error * Vibration in industrial processes * Ambient temperature and humidity * Insufficient training * Normal wear and tear * Variability in settings * Computer response time * Incompetent employee ==Importance to economics== [[John Maynard Keynes]] and [[Frank Knight]] both discussed the inherent unpredictability of economic systems in their work and used it to criticise the mathematical approach to [[economics]], in terms of expected [[utility]], developed by [[Ludwig von Mises]] and others. [[John Maynard Keynes|Keynes]] in particular argued that economic systems did not automatically tend to the equilibrium of full employment owing to their agents' inability to predict the future. As he remarked in ''[[General Theory of Employment, Interest and Money|The General Theory of Employment, Interest and Money]]'': ''… as living and moving beings, we are forced to act … [even when] our existing knowledge does not provide a sufficient basis for a calculated mathematical expectation.'' [[John Maynard Keynes|Keynes]]'s thinking was at odds with the [[classical liberalism]] of the [[Austrian school]] of [[economist]]s, but [[G. L. S. Shackle]] recognised the importance of [[John Maynard Keynes|Keynes]]'s insight and sought to formalise it within a [[free-market]] philosophy. ==Importance to industrial management== [[Harry Alpert]] observed: ''A riot occurs in a certain prison. Officials and sociologists turn out a detailed report about the prison, with a full explanation of why and how it happened here, ignoring the fact that the causes were common to a majority of prisons, and that the riot could have happened anywhere.'' The quote recognises that there is a temptation to react to an extreme outcome and to see it as significant, even where its causes are common to many situations and the distinctive circumstances surrounding its occurrence, the results of mere chance. Such behaviour has many implications within management, often leading to interventions in processes that merely increase the level of variation and frequency of undesirable outcomes. [[W. Edwards Deming|Deming]] and [[Walter A. Shewhart|Shewhart]] both advocated the [[control chart]] as a means of managing a [[business process]] in an economically efficient manner. ==Importance to statistics== ===Deming and Shewhart=== Within the [[frequency probability]] framework, there is no process whereby a [[probability]] can be attached to the future occurrence of special cause. However the [[bayesian probability|Bayesian]] approach does allow such a [[probability]] to be specified. The existence of special-cause variation led [[John Maynard Keynes|Keynes]] and [[W. Edwards Deming|Deming]] to an interest in [[bayesian probability]] but no formal synthesis has ever been forthcoming. Most statisticians of the Shewhart-Deming school take the view that special causes are not embedded in either experience or in current thinking (that's why they come as a surprise) so that any subjective probability is doomed to be hopelessly badly [[calibration (probability)|calibrated]] in practice. It is immediately apparent from the [[Gottfried Leibniz|Leibniz]] quote above that there are implications for [[sampling (statistics)|sampling]]. [[W. Edwards Deming|Deming]] observed that in any forecasting activity, the [[population (statistics)|population]] is that of future events while the [[sampling frame]] is, inevitably, some [[subset]] of historical events. [[W. Edwards Deming|Deming]] held that the disjoint nature of [[population (statistics)|population]] and [[sampling frame]] was inherently problematic once the existence of special-cause variation was admitted, rejecting the general use of [[probability]] and conventional [[statistics]] in such situations. He articulated the difficulty as the distinction between [[analytic and enumerative statistical studies]]. [[Walter A. Shewhart|Shewhart]] argued that, as processes subject to special-cause variation were inherently unpredictable, the usual techniques of probability could not be used to separate special-cause from common-cause variation. He developed the [[control chart]] as a statistical [[heuristic]] to distinguish the two types of variation. Both [[W. Edwards Deming|Deming]] and [[Walter A. Shewhart|Shewhart]] advocated the [[control chart]] as a means of assessing a process's state of [[statistical control]] and as a foundation for forecasting. ===Keynes=== [[John Maynard Keynes|Keynes]] identified three domains of [[probability]]: *[[Frequency probability]]; *Subjective or [[Bayesian probability]]; and *Events lying outside the possibility of any description in terms of [[probability]] (special causes) - and sought to base a [[probability theory]] thereon. == References == {{reflist}} ==Bibliography== *Deming, W E (1975) On probability as a basis for action, ''The American Statistician'', 29(4), pp146-152 *Deming, W E (1982) ''Out of the Crisis: Quality, Productivity and Competitive Position'' ISBN 0-521-30553-5 *Keynes, J M (1921) ''A Treatise on Probability'', ISBN 0-333-10733-0 *Keynes, J M (1936) ''The General Theory of Employment, Interest and Money'' ISBN 1-57392-139-4 *Knight, F H (1921) ''Risk, Uncertainty and Profit'' ISBN 1-58798-126-2 *Shackle, G L S (1972) ''Epistemics and Economics: A Critique of Economic Doctrines'' ISBN 1-56000-558-0 *Shewhart, W A (1931) ''Economic Control of Quality of Manufactured Product'' ISBN 0-87389-076-0 *Shewhart, W A (1939) ''Statistical Method from the Viewpoint of Quality Control'' ISBN 0-486-65232-7 *Wheeler, D J & Chambers, D S (1992) ''Understanding Statistical Process Control'' ISBN 0-945320-13-2 == See also == * [[Corrective and Preventative Action]] (CAPA) [[Category:Econometrics]] [[Category:Quality control]] [[Category:Statistical models]]