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P-curve won’t do your laundry, but it will distinguish replicable from non-replicable findings in observational research: Comment on Bruns & Ioannidis (2016)

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  • Uri Simonsohn
  • Leif D Nelson
  • Joseph P Simmons

Abstract

p-curve, the distribution of significant p-values, can be analyzed to assess if the findings have evidential value, whether p-hacking and file-drawering can be ruled out as the sole explanations for them. Bruns and Ioannidis (2016) have proposed p-curve cannot examine evidential value with observational data. Their discussion confuses false-positive findings with confounded ones, failing to distinguish correlation from causation. We demonstrate this important distinction by showing that a confounded but real, hence replicable association, gun ownership and number of sexual partners, leads to a right-skewed p-curve, while a false-positive one, respondent ID number and trust in the supreme court, leads to a flat p-curve. P-curve can distinguish between replicable and non-replicable findings. The observational nature of the data is not consequential.

Suggested Citation

  • Uri Simonsohn & Leif D Nelson & Joseph P Simmons, 2019. "P-curve won’t do your laundry, but it will distinguish replicable from non-replicable findings in observational research: Comment on Bruns & Ioannidis (2016)," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-5, March.
    • Handle: RePEc:plo:pone00:0213454
    DOI: 10.1371/journal.pone.0213454
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    Cited by:

    1. Yip, Jeremy A. & Schweitzer, Maurice E., 2022. "Norms for Behavioral Change (NBC) model: How injunctive norms and enforcement shift descriptive norms in science," Organizational Behavior and Human Decision Processes, Elsevier, vol. 168(C).
    2. Adler, Susanne Jana & Röseler, Lukas & Schöniger, Martina Katharina, 2023. "A toolbox to evaluate the trustworthiness of published findings," Journal of Business Research, Elsevier, vol. 167(C).

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