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. The lesson here is this: beware of so-called “statistically significant” results. On Australias east coast, the tropical seagrass Zostera muelleri ssp. Such ex post results, however, are often just spurious correlations. Coastal seagrass habitats are at risk from a range of anthropogenic activities that modify the natural light environment, including dredging activities associated with coastal and port developments. 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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