FREE INTERACTIVE TOOL
Effect Size Calculator
Convert between effect size measures, extract an effect size from reported statistics, or calculate one from your group data.
THREE WAYS THIS TOOL CAN HELP
1
Convert
You have an effect size in one form — for example Cohen's d — and need it in another form — for example r or eta squared (η²). Use this path to convert between common effect size measures.
2
Extract
Your analysis output or a published paper reports a test statistic such as t, F, or χ² and you need to work out the effect size from it. Use this path to extract an effect size from reported results.
3
Calculate
You have the means and standard deviations from two groups and want to know how big the difference is. Use this path to calculate Cohen's d directly from your group data.
Need my information on effect sizes? Read more in the About this tool section below →
ABOUT THIS TOOL
How the Effect Size Calculator
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An effect size is a number that tells you how large and meaningful a result really is — not just whether it is statistically significant. A p-value tells you whether a result is unlikely to be due to chance. An effect size tells you how big the difference, relationship, or change actually is in practical terms.
This calculator helps with three common effect size tasks — converting between different effect size measures, extracting an effect size from a reported test statistic, and calculating an effect size directly from group means and standard deviations. It is particularly useful when planning a study and needing an effect size estimate for sample size calculations, or when writing up results and needing to report a standardised effect size.
Because different statistical tests use different effect size measures, many students and researchers find this area confusing. This calculator brings the most common options together in one place and helps you move between them more easily.
Effect size calculations involve assumptions about the data and the measures used. Results should always be interpreted in context and in light of your research question, study design, and the conventions in your discipline. For complex or high-stakes analyses always consult a qualified statistician.
Frequently asked questions about effect sizes
An effect size is a measure that shows how large or meaningful a result is. While a p value tells you whether a result is statistically significant, an effect size helps you understand the size of the difference, relationship, or association. This is important because a result can be statistically significant but still be very small in practical terms, especially in a large sample.
One of the most commonly reported effect size measures is Cohen’s d. Cohen’s d is usually used to describe the size of the difference between two group means. For example, if students taught with a new method scored higher than those taught with the usual method, Cohen’s d would indicate whether that difference was small, moderate, or large. As a rough guide, 0.20 is often described as a small effect, 0.50 as a medium effect, and 0.80 as a large effect, although these values should always be interpreted in context.
Effect sizes are important because they help readers understand not just whether a result exists, but how much it matters.
Effect size is important because it helps you judge the practical importance of a result, not just whether it reached statistical significance. A result can be statistically significant but still be very small in real-world terms. Reporting an effect size gives a fuller picture of your findings and is now expected in many research reports, theses, and journal articles.
Yes, often you can, provided the paper reports enough statistical information. For example, if a paper includes means, standard deviations, sample sizes, or key test statistics, it may be possible to calculate or estimate an effect size. This can be useful when reviewing previous studies or comparing your findings with earlier research.
In some cases, yes. Certain effect size measures can be converted into other forms, depending on the information available and the assumptions involved. This can be helpful when comparing studies or when you need to report your findings in a format commonly used in your field. This calculator is designed to support common conversion tasks where appropriate.
There are commonly used guidelines to help interpret effect sizes, but these should be treated as rough rules of thumb rather than strict cut-offs. For Cohen's d, values around 0.20 are often described as small, 0.50 as medium, and 0.80 as large. Similar guidelines exist for other effect size measures, but the exact interpretation depends on the statistic being used. Effect sizes should always be interpreted in context — a small effect may be very meaningful in some research areas and less so in others.