What is a p-curve?

• A. A curve depicting the distribution of effect sizes across studies.
• B. A curve summarizing the frequency distribution of p-values you'd expect to see in published research.
• C. A plot showing the relationship between sample size and p-values.
• D. A chart predicting p-values from group means.

Answer: (B)

A p-curve is a graphical summary of how often different p-values appear across published studies, usually using the significant p-values (p < .05). It helps researchers assess whether the set of significant findings shows evidential value or evidence of questionable practices like p-hacking or selective reporting. If there’s a real effect and good methods, the curve tends to have more very small p-values (left side) and fewer near the 0.05 cutoff. If there’s no true effect, the p-values among those reported as significant would show a more uniform or flat appearance; p-hacking would pile up just under 0.05. This description matches the idea of summarizing the frequency distribution of p-values you’d expect to see in published research, unlike curves that depict effect sizes, sample size relationships, or predictions from group means.