Effect Size Calculator
Cohen's d, Hedges' g and r — from group stats or a t-value.
Enter both groups' mean, SD and sample size.
Benchmarks (Cohen): d = 0.2 small, 0.5 medium, 0.8 large · r = 0.1 small, 0.3 medium, 0.5 large. Interpret against your field's norms — benchmarks are conventions, not laws.
Ethical, compliant guidance: We provide academic support, mentoring, analysis, editing and structuring — not authorship. Your work stays compliant with university policies.
About this tool
A p-value says a difference probably exists; an effect size says whether it is big enough to matter. Journals, reviewers and examiners now expect both — APA style has required effect sizes for years, and 'significant but trivial' findings are a standard rejection reason.
This calculator produces Cohen's d (with the Hedges' g small-sample correction) and the equivalent correlation r from either your groups' descriptive statistics or a t-statistic you already have — and converts between d and r for meta-analysis and comparison purposes.
Everything runs in your browser; nothing you enter is stored or sent anywhere.
How to use it
- 1Pick your input: group means/SDs/sizes, a t-statistic with its degrees of freedom, or an existing d or r to convert.
- 2Enter the values from your SPSS output or descriptive table.
- 3Read d, Hedges' g and r with their conventional interpretation (small/medium/large).
- 4Report the effect size alongside the p-value in your results chapter — e.g. t(58) = 2.31, p = .024, d = 0.61.
Frequently asked
Should I report Cohen's d or Hedges' g?+
They answer the same question; g corrects d's slight upward bias in small samples. With group sizes under ~20, prefer g; with larger samples they are practically identical. Report whichever you name consistently.
What is a good effect size?+
Cohen's conventions — d of 0.2 small, 0.5 medium, 0.8 large; r of 0.1/0.3/0.5 — are defaults, not laws. In fields where interventions are hard to move (education, health behaviour), a d of 0.3 can be practically important. Interpret against comparable studies in your area.
Why do reviewers ask for effect sizes when my p-value is significant?+
Because p-values conflate effect magnitude with sample size — a huge sample makes trivial differences 'significant'. The effect size separates the two, telling readers whether the finding matters and enabling future meta-analysis and power calculations.
Go deeper
Statistics & Data Analysis guidesHow to Choose the Right Statistical Test: A Decision Guide
How to Analyse Likert Scale Data in SPSS (Without Getting It Wrong)
Mediation vs Moderation Analysis: What They Mean and How to Run Them
What Is Jamovi? The Free SPSS Alternative, Honestly Reviewed
Not sure where to start?
Book a free 15-minute consult. We'll map your next three steps — no obligation.