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Holm-Bonferroni Multiple Comparisons Correction In Minutes

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Running holm-bonferroni multiple comparisons correction analysis...

Running holm-bonferroni multiple comparisons correction analysis...

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How it works

Run pairwise Welch t-tests across all group pairs for each outcome, then apply Holm step-down correction across all tests simultaneously to control the family-wise error rate while maximising statistical power.

Use when comparing a numeric outcome across 3 or more groups and you need to run all pairwise comparisons while controlling the false-positive rate — common in education, clinical, and A/B testing contexts.

Do not use if you have only two groups (a single t-test suffices) or if you want to control the false discovery rate (FDR) rather than the family-wise error rate.

What data do you need?

Dataset with 4 columns

group (categorical) outcome_1 (numeric) outcome_2 (numeric) outcome_3 (numeric)

Minimum 30 rows

What's in the report?

Run pairwise t-tests across all parental education levels for math/reading/writing scores, then apply Holm-Bonferroni correction. Show which differences survive after controlling family-wise error rate.

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Group Descriptive Statistics

Mean scores and sample sizes per parental education level — shows which groups score highest and lowest before significance testing.

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Mean Scores by Parental Education Level

Grouped mean scores across all education levels and outcomes — reveals whether the ranking of groups is consistent across math, reading, and writing.

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Score Distributions by Education Level

Box plots of score distributions per group — shows within-group variability and distribution overlap that determines test power.

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Pairwise Comparison Results (All 45 Tests)

Full table of all 45 pairwise Welch t-tests with raw and Holm-adjusted p-values and significance decisions.

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Significant Pairwise Differences After Correction

Mean score gaps for pairs that survive Holm-Bonferroni correction — highlights the largest education-level effects.

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Adjusted P-Value Significance Map

Heatmap of average adjusted p-values across all group pairs — shows which pairs are consistently significant across all three outcomes.

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AI Insights

Plain-English interpretation — what the numbers mean, what's significant, and what to do next.

The Question This Answers

See objective

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Questions?

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