How to use
- Choose the metric first: Cohen's d, Hedges' g, or eta-squared.
- Enter two-group summaries for d or g, or choose ANOVA sums or group summaries for eta-squared.
- Read the effect value and the rough magnitude guide together with the context of your field and decision.
Wave 3 statistics expansion
Three effect-size workflows from summary inputs
Use Cohen's d for a standardized mean difference between two groups when you want a quick practical-size view next to a t-test result.
Inputs
Use this when you have two independent groups and want a standardized mean difference beside a t-test result.
Use this when you want the same standardized mean-difference logic as Cohen's d but with a small-sample correction.
Use eta-squared as a simple one-way ANOVA effect-size summary. You can enter ANOVA sums directly or derive them from group summaries.
Use one line per group. Separate fields with commas, semicolons, tabs, or label: n mean sd.
What the page means by effect size
- Cohen's d standardizes the mean difference by a pooled within-group spread.
- Hedges' g applies a small-sample correction to Cohen's d.
- Eta-squared summarizes the share of total sample variance associated with group membership in one-way ANOVA.
- The rough labels small, medium, and large are only conventions. They do not replace domain judgment.
Effect size vs p-value
A p-value tells you how compatible the observed data are with a null model. Effect size tells you how large the observed pattern is on a standardized or variance-share scale. A very small effect can still have a low p-value in a large sample, and a practically important effect can have an uncertain p-value in a small sample.
Use this page when you need the magnitude view next to a test result, not as a replacement for study design checks, confidence intervals, or domain-specific interpretation.
Where this fits in a statistics workflow
- T-Test CalculatorUse this to test mean differences, then bring the summary statistics here for a standardized magnitude view.
- ANOVA CalculatorUse this to run the overall F test, then use eta-squared here if you want the variance-share summary in a dedicated page.
- Correlation CalculatorUse correlation when the first question is association strength rather than group difference.
- Statistics (inference & tests)Return to the hub for neighboring planning, inference, and regression pages.
FAQ
Why calculate effect size if I already have a p-value?
A p-value tells you how compatible the data are with a null hypothesis. Effect size tells you how large the observed difference or explained variance is on a practical scale. They answer different questions and should be read together.
What is the difference between Cohen's d and Hedges' g?
Both summarize standardized mean differences. Hedges' g applies a small-sample correction to Cohen's d, so it is often preferred when sample sizes are modest.
Should I treat small, medium, and large as fixed truth?
No. Those labels are rough conventions, not universal rules. Practical importance depends on the field, outcome, cost of change, and decision context.
When should I use eta-squared?
Use eta-squared as a simple one-way ANOVA effect-size summary when you want the share of total sample variance associated with group membership. It complements the ANOVA F test but does not replace the need to inspect design assumptions or follow-up comparisons.
Does the share URL include my numeric inputs?
No. The share URL stores only lightweight settings such as the active effect-size mode and eta-squared input source. Entered numeric values stay in your browser.
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