![]() 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. 2 The more inferences are made, the more likely erroneous inferences become. Such ex post results, however, are often just spurious correlations. In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously 1 or infers a subset of parameters selected based on the observed values. Data scientists can then form hypotheses about why these relationships exist. If you just take a pile of data that shares one common element (the thing you are interested in), you will likely find some other common elements purely by chance. 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. Data dredging (also known as data snooping or p-hacking) is the misuse of data analysis to find patterns in data that can be presented as statistically significant, thus dramatically increasing and understating the risk of false positives. Data dredging - sometimes referred to as data fishing - is a data mining practice in which large data volumes are analyzed to find any possible relationships between the data. Data dredging is simply this - sifting through a big ole' pile of numbers without a hypothesis, eyeballing things that appear to be related but might just be random chance. 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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