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Fishing for Data (Post-Designation) |
| Description: | ||
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| The argument draws a conclusion from correlations observed in a sample, but only after the sample has already been drawn, and without declaring in advance what correlations the experimenter was expecting to find. | ||
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| Examples: | ||
| "Three of my four children were born in February, and all three were left-handed. Apparently most people born in February are left-handed." | ||
| "We took a survey of our class and discovered that, out of 30 students, seven were born in January. We conclude that college students are much more likely to be born in January than in any other month." | ||
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| Discussion: | ||
| Any sample is likely to have certain peculiarities. It is unlikely that
the peculiarities of a given sample will happen to support a pre-designated
hypothesis, but it will always be possible to find some hypothesis or other for
the data to support. That is why good experimental method requires that the
"experimental hypothesis" (and its opposite, the "null hypothesis") be
clearly stated before looking at the data. Actually, using the peculiarities found in samples to suggest new lines of research may not be a bad idea. However, when this is done, the sample is used as the minor premiss of a Retroduction, not as the major premiss of an Induction. The conclusion should not be a generalization from the sample to the class sampled; the conclusion should suggest a mechanism to explain the observed peculiarities in the sample. |
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| Classification: An inductive Fallacy of Circularity. | ||
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| Source: Charles S. Peirce describes this as one of the chief errors in inductive logic. | ||
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