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20. P-Curving the Evidence: P-Values Published in Human Factors (2017-2023)
DescriptionPublication bias and questionable research practices can inflate the perceived credibility of reported scientific findings, leading to low replicability. This preregistered study aimed to estimate the evidentiary value of empirical findings published in Human Factors journal (2017-2023) using two meta-analytic methods: p-curve analysis to determine if the significant p-values reflect true effects or selective reporting, and Bayesian mixture modeling of p-value distributions to gauge the extent of contamination from the null hypothesis. Empirical findings from 64 articles were included in the present analyses. P-curve results indicated evidential value, ruling out high levels of selective reporting as an explanation for significant results. Mixture modeling estimated a modest 25% contamination rate by the null hypothesis among significant p-values. Results document the quality of empirical evidence reported in Human Factors.
Event Type
Poster
TimeThursday, September 12th5:30pm - 6:30pm MST
LocationMcArthur Ballroom
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