Cherry picking (fallacy)

Cherry picking, suppressing evidence, or the fallacy of incomplete evidence is the act of pointing to individual cases or data that seem to confirm a particular position, while ignoring a significant portion of related cases or data that may contradict that position. It is a kind of fallacy of selective attention, the most common example of which is the confirmation bias. Cherry picking may be committed unintentionally.

The term is based on the perceived process of harvesting fruit, such as cherries. The picker would be expected to only select the ripest and healthiest fruits. An observer who only sees the selected fruit may thus wrongly conclude that most, or even all, of the fruit is in such good condition.

Cherry picking can be found in many logical fallacies. For example, the "fallacy of anecdotal evidence" tends to overlook large amounts of data in favor of that known personally, "selective use of evidence" rejects material unfavorable to an argument, while a false dichotomy picks only two options when more are available.

In statistics

Cherry picking can refer to the selection of data or data sets so a study or survey will give desired, predictable results which may be misleading or even completely contrary to actuality.

In politics

In his book The Years of Talking Dangerously, linguist Geoffrey Nunberg wrote that, "By cherry-picking his dictionaries and definitions, [Assistant Attorney General Jay] Bybee managed to come up with a definition of torture that ruled out any practice that doesn't cause lasting impairment or inflict pain that rises to the level of death or organ damage."

In science

Choosing to make selective choices among competing evidence, so as to emphasize those results that support a given position, while ignoring or dismissing any findings that do not support it, is a practice known as "cherry picking" and is a hallmark of poor science or pseudo-science.

— Richard Somerville, Testimony before the US House of Representatives Committee on Energy and Commerce Subcommittee on Energy and Power, March 8, 2011.

Good science looks at all the evidence (rather than cherry picking only favorable evidence), controls for variables so we can identify what is actually working, uses blinded observations so as to minimize the effects of bias, and uses internally consistent logic."

In medicine

In a 2002 study, researchers "reviewed 31 antidepressant efficacy trials to identify the primary exclusion criteria used in determining eligibility for participation. Their findings suggest that patients in current antidepressant trials represent only a minority of patients treated in routine clinical practice for depression. Excluding potential clinical trial subjects with certain profiles means that the ability to generalize the results of antidepressant efficacy trials lacks empirical support, according to the authors."

See also

• Biased sample
• Confirmation bias
• Proof by example
• Fallacy of quoting out of context
• Golden sample
• Hasty generalization
• Informal fallacy
• Selection bias