The regression (or regressive) fallacy is an informal fallacy. It assumes that something has returned to normal because of corrective actions taken while it was abnormal. This fails to account for natural fluctuations. It is frequently a special kind of the post hoc fallacy.
Explanation
Things like golf scores, the earth's temperature, and chronic back pain fluctuate naturally and usually regress towards the mean. The logical flaw is to make predictions that expect exceptional results to continue as if they were average (see Representativeness heuristic). People are most likely to take action when variance is at its peak. Then after results become more normal they believe that their action was the cause of the change when in fact it was not causal.
This use of the word "regression" was coined by Sir Francis Galton in a study from 1885 called "Regression Toward Mediocrity in Hereditary Stature". He showed that the height of children from very short or very tall parents would move towards the average. In fact, in any situation where two variables are less than perfectly correlated, an exceptional score on one variable may not be matched by an equally exceptional score on the other variable. The imperfect correlation between parents and children (height is not entirely heritable) means that the distribution of heights of their children will be centered somewhere between the average of the parents and the average of the population as whole. Thus, any single child can be more extreme than the parents, but the odds are against it.
Examples
When his pain got worse, he went to a doctor, after which the pain subsided a little. Therefore, he benefited from the doctor's treatment.
The pain subsiding a little after it has gotten worse is more easily explained by regression towards the mean. Assuming the pain relief was caused by the doctor is fallacious.
The student did exceptionally poorly last semester, so I punished him. He did much better this semester. Clearly, punishment is effective in improving students' grades.
Often exceptional performances are followed by more normal performances, so the change in performance might better be explained by regression towards the mean. Incidentally, some experiments have shown that people may develop a systematic bias for punishment and against reward because of reasoning analogous to this example of the regression fallacy.[1]
The frequency of accidents on a road fell after a speed camera was installed. Therefore, the speed camera has improved road safety.
Speed cameras are often installed after a road incurs an exceptionally high number of accidents, and this value usually falls (regression to mean) immediately afterwards. Many speed camera proponents attribute this fall in accidents to the speed camera, without observing the overall trend.
Some authors use the Sports Illustrated cover jinx as an example of a regression effect: extremely good performances are likely to be followed by less extreme ones, and athletes are chosen to appear on the cover of Sports Illustrated only after extreme performances. Attributing this to a "jinx" rather than regression, as some athletes reportedly believe, is an example of committing the regression fallacy.[2]
Misapplication
On the other hand, dismissing valid explanations can lead to a worse situation. For example:
After the Western Allies invaded Normandy, creating a second major front, German control of Europe waned. Clearly, the combination of the Western Allies and the USSR drove the Germans back.
Fallacious evaluation: "Given that the counterattacks against Germany occurred only after they had conquered the greatest amount of territory under their control, regression to the mean can explain the retreat of German forces from occupied territories as a purely random fluctuation that would have happened without any intervention on the part of the USSR or the Western Allies." However, this was not the case. The reason is that political power and occupation of territories is not primarily determined by random events, making the concept of regression to the mean inapplicable (on the large scale).
In essence, misapplication of regression to the mean can reduce all events to a "just so" story, without cause or effect. (Such misapplication takes as a premise that all events are random, as they must be for the concept of regression to the mean to be validly applied.)
Notes
Schaffner, 1985; Gilovich, 1991 pp. 27–28
Gilovich, 1991 pp. 26–27; Plous, 1993 p. 118
References
Friedman, Milton (1992). "Do Old Fallacies Ever Die?". Journal of Economic Literature. 30 (4): 2129–2132. JSTOR 2727976.
Gilovich, Thomas (1991). How we know what isn't so: The fallibility of human reason in everyday life. New York: The Free Press. ISBN 0029117054.
Plous, Scott (1993). The Psychology of Judgment and Decision making. New York: McGraw-Hill. ISBN 0070504776.
Quah, Danny (1993). "Galton's Fallacy and Tests of the Convergence Hypothesis". The Scandinavian Journal of Economics. 95 (4): 427–433. doi:10.2307/3440905. hdl:1721.1/63653. JSTOR 3440905.
Schaffner, P.E. (1985). "Specious learning about reward and punishment". Journal of Personality and Social Psychology. 48 (6): 1377–86. doi:10.1037/0022-3514.48.6.1377.
External links
Fallacy files: Regression fallacy
vte
Fallacies (list)
Formal
In propositional logic
Affirming a disjunct Affirming the consequent Denying the antecedent Argument from fallacy
In quantificational logic
Existential Illicit conversion Proof by example Quantifier shift
Syllogistic fallacy
Affirmative conclusion from a negative premise Exclusive premises Existential Necessity Four terms Illicit major Illicit minor Negative conclusion from affirmative premises Undistributed middle
Masked man Mathematical fallacy
Informal
Equivocation
Equivocation False equivalence False attribution Quoting out of context Loki's Wager No true Scotsman Reification
Question-begging fallacies
Circular reasoning / Begging the question Loaded language
Leading question Compound question / Loaded question / Complex question No true Scotsman
Correlative-based fallacies
False dilemma
Perfect solution Denying the correlative Suppressed correlative
Illicit transference
Composition Division Ecological
Secundum quid
Accident Converse accident
Faulty generalization
Anecdotal evidence Sampling bias
Cherry picking McNamara Base rate / Conjunction Double counting False analogy Slothful induction Overwhelming exception
Vagueness / ambiguity
Accent False precision Moving the goalposts Quoting out of context Slippery slope Sorites paradox Syntactic ambiguity
Questionable cause
Animistic
Furtive Correlation implies causation
Cum hoc Post hoc Gambler's
Inverse Regression Single cause Slippery slope Texas sharpshooter
Fallacies of relevance
Appeals to emotion
Fear Flattery Novelty Pity Ridicule Think of the children In-group favoritism Invented here / Not invented here Island mentality Loyalty Parade of horribles Spite Stirring symbols Wisdom of repugnance
Genetic fallacies
Ad hominem
Appeal to motive Association
Reductio ad Hitlerum
Godwin's law Reductio ad Stalinum Bulverism Poisoning the well Tone Tu quoque Whataboutism
Authority
Accomplishment Ipse dixit Poverty / Wealth Etymology Nature Tradition / Novelty
Chronological snobbery
Appeals to consequences
Argumentum ad baculum Wishful thinking
Ad nauseam Argument to moderation Argumentum ad populum Appeal to the stone / Proof by assertion Ignoratio elenchi Argument from silence Invincible ignorance Moralistic / Naturalistic Motte-and-bailey fallacy Rationalization Red herring
Two wrongs make a right Special pleading Straw man Cliché I'm entitled to my opinion
Category Category
Undergraduate Texts in Mathematics
Graduate Studies in Mathematics
Hellenica World - Scientific Library
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