Testing hypotheses suggested by the data
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In statistics, hypotheses suggested by a given dataset, when tested with the same dataset that suggested them, are likely to be accepted even when they are not true. This is because circular reasoning (double dipping) would be involved: something seems true in the limited data set, therefore we hypothesize that it is true in general, therefore we (wrongly) test it on the same limited data set, which seems to confirm that it is true. Generating hypotheses based on data already observed, in the absence of testing them on new data, is referred to as post hoc theorizing (from Latin post hoc, "after this").
The correct procedure is to test any hypothesis on a data set that was not used to generate the hypothesis.
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See also
- Bonferroni correction
- Data analysis
- Data dredging
- Exploratory data analysis
- Post-hoc analysis
- Predictive analytics
- Texas sharpshooter fallacy
- Type I and type II errors
- Uncomfortable science
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