![]() ![]() A fishing expedition is the indiscriminate testing of associations between different combinations of variables not with specific hypotheses in mind but with the hope of finding something that is statistically significant in the data. ![]() P-hacking is the relentless analysis of data with an intent to obtain a statistically significant result, usually to support the researcher’s hypothesis. Cherry-picking is the presentation of favorable evidence with the concealment of unfavorable evidence. HARKing (Hypothesizing After the Results are Known) is the presentation of a post hoc hypothesis as an a priori hypothesis. To avoid perpetrating this form of data fraud (and reduce positive-results bias to boot), some journals and funding organizations are now requiring researchers to preregister their clinical trials, stating in advance what hypotheses they are going to be testing.Questionable research practices (QRPs) in the statistical analysis of data and in the presentation of the results in research papers include HARKing, cherry-picking, P-hacking, fishing, and data dredging or mining. The lesson here is this: beware of so-called “statistically significant” results. Such ex post results, however, are often just spurious correlations. In the words of Wikipedia: “The process of data dredging involves automatically testing huge numbers of hypotheses about a single data set by exhaustively searching … for combinations of variables that might show a correlation ….” This form of data fraud thus occurs when researchers perform multiple statistical tests on a single set of data and then selectively publish only those results that satisfy some test of statistical significance. ![]() Let’s proceed with our parade of fraudulent data practices, shall we? Next up is data dredging (a/k/a “p-hacking”), a more sophisticated (and less transparent) form of cherry picking. ![]()
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