A great and timely tool to visualize this is available at 538 Science. By simply manipulating researcher degrees of freedom, even absolutely negative data can produce a p-value under 0.05 an incredible 61% of the time. These include when to stop collecting data, whether or not your data will be transformed, which statistical tests (and parameters) will be used, and so on. P-hacking is typically done through manipulation of “researcher degrees of freedom,” or the decisions made by the investigator. The term p-hacking, coined in 2014 by Regina Nuzzo in Nature News, describes the conscious or subconscious manipulation of data in a way that produces a desired p-value. Rather, I want to talk about something which we all have experience with, to some degree or another: data dredging, or p-hacking. Moreover, the p-value cannot directly speak to the strength of evidence, which can be better inferred when considering effect size, prior probability, and experimental reproducibility.īut this isn’t an article about the p-value, per se. There are important qualifications to p-value interpretation. Contrary to popular interpretation, the p-value doesn’t indicate the likelihood that the observed result was due to chance. P-value abuse directly contributes to one of the biggest problems facing the scientific community: the prominence of false-positive results in the published literature.
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