Definition: P-curve is a tool for identifying potential publication bias and makes use of the distribution of significant p-values in a series of independent findings. The deviation from the expected right-skewed distribution can be used to assess the existence and degree of publication bias: if the curve is right-skewed, there are more low, highly significant p-values, reflecting an underlying true effect. If the curve is left-skewed, there are many barely significant results just under the 0.05-threshold. This suggests that the studies lack evidential value and may be underpinned by questionable research practices (QRPs; e.g., p-hacking). In the case of no true effect present (true null hypothesis) and unbiased p-value reporting, the p-curve should be a flat, horizontal line, representing the typical distribution of p-values.
Publication bias (File Drawer Problem),
Questionable Research Practices or Questionable Reporting Practices (QRPs),
References: Bruns and Ioannidis (2016), Simonsohn et al. (2014a), Simonsohn et al.(2014b), & Simonsohn et al. (2019)
Drafted and Reviewed by: Bettina M. J. Kern, Sam Guay, Kamil Izydorczak, Charlotte R. Pennington, Robert M. Ross, Olmo van den Akker
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