The predominant reason for this practice is the widespread notion among academics that “statistically significant data is noteworthy, and one that is not statistically significant is not”. We may use these term interchangeably in the discussion below. Data dredging is recognized by several names such as ‘fishing trip’, ‘data snooping’, ‘p-hacking’ and so on. This may lead to an exponential increase in the risk of inclusion of large quantities of false positive results, thereby corrupting the data that was meant to be originally reported.

Impact of data dredging on epidemiologyĭata dredging is defined as “cherry-picking of promising findings leading to a spurious excess of statistically significant results in published or unpublished literature”.

The following discussion will attempt to define data dredging and provide an answer to such questions. 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.What is data dredging? How does it affect the p-value? What is its impact on the world around us? 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.

Data dredging/p-hacking