Abstract
Replicable and reliable research is essential for cumulative science and its applications in practice. This article examines the quality of research on dishonesty using a sample of 286 hand-coded test statistics from 99 articles. Z-curve analysis indicates a low expected replication rate, a high proportion of missing studies, and an inflated false discovery risk. Test of insufficient variance (TIVA) finds that 11/61 articles with multiple test statistics contain results that are ``too-good-to-be-true''. Sensitivity analysis confirms the robustness of the findings. In conclusion, caution is advised when relying on or applying the existing literature on dishonesty.
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