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Some scholars classify cherry-picking as a fallacy of selective attention, the most common example of which is the confirmation bias. [3] 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 reality. [4]
The Texas sharpshooter fallacy is an informal fallacy which is committed when differences in data are ignored, but similarities are overemphasized. From this reasoning, a false conclusion is inferred. [1] This fallacy is the philosophical or rhetorical application of the multiple comparisons problem (in statistics) and apophenia (in cognitive ...
Data dredging is an example of disregarding the multiple ... Cherry picking – Fallacy of ... Tortured Data, and Other Ways to Lie with Statistics.
Cherry picking, which actually is not selection bias, but confirmation bias, when specific subsets of data are chosen to support a conclusion (e.g. citing examples of plane crashes as evidence of airline flight being unsafe, while ignoring the far more common example of flights that complete safely.
Cherry picking (suppressed evidence, incomplete evidence, argument by half-truth, fallacy of exclusion, card stacking, slanting) – using individual cases or data that confirm a particular position, while ignoring related cases or data that may contradict that position.
“Unfortunately, he is cherry picking data points to make inaccurate comparisons and draw false conclusions in support of a false narrative,” he said. Show comments Advertisement
In particular, the three researchers objected to the then widespread practice of cherry-picking data and attempts to make insignificant results appear statistically credible, especially an approach for which they coined the term p-hacking in a 2014 paper. [2] [3] [4]
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