Too Good to be False: Nonsignificant Results Revisited

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Description: A project on P-value distributions, and how these can be used to estimate effects even when individual p-values are non-significant. All files are pulled from the Github repository and updated on the go.

License: CC0 1.0 Universal

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osf.io/qpfnw

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  • Preprint of Too good to be false: Nonsignificant results revisited

    Due to its probabilistic nature, Null Hypothesis Significance Testing (NHST) is subject to decision errors. The concern for false positives has oversh...

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