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Upload a CSV with one row per test, a column of p-values and, if you have one, a label for each test, and this tool shows which results still count after Bonferroni, Holm and Benjamini-Hochberg corrections.
Business example: a headline test (Upworthy) → · Business example: a donor letter test (Karlan and List) → · Analyst example: correlations among wine measurements (Cortez et al.) →
Data: J. Nathan Matias, Kevin Munger, Marianne Aubin Le Quere and Charles Ebersole (2021), The Upworthy Research Archive, a time series of experiments in U.S. Media, Open Science Framework, osf.io/jd64p, CC BY 4.0. We used one test of 12 headlines and compared each with the first. Charts by MCP Analytics, computed from this data.
Data: Dean Karlan and John A. List (2007), Does Price Matter in Charitable Giving?, Harvard Dataverse, doi:10.7910/DVN/27853. Licence: CC0 1.0. Charts by MCP Analytics, computed from this data.
The practical guide behind this tool: when it applies, how to read the output, and the traps that make it say the wrong thing.
Also in this set: Holm-Bonferroni method · Benjamini-Hochberg procedure · The multiple comparisons problem
Which results survive correction for multiple testing? Bonferroni, Holm and Benjamini-Hochberg, strict and lenient.
Complete analysis with charts, tables, and diagnostics.
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MCP Analytics. Multiple comparisons. Free tool. https://mcpanalytics.ai/free/standard_multiple_comparisons_v2. Accessed [date you used it].
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