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I² Heterogeneity Calculator for Meta-Analysis

Calculate I², Cochran's Q, τ² and the 95% prediction interval from your studies, with a plain-language interpretation based on the Cochrane Handbook.

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Effect measure
Data entry
Model
StudyEvents (treatment)No event (treatment)Events (control)No event (control)

Results (random effects, 6 studies)

Pooled risk ratio0.37 (95% CI 0.24 to 0.58)
Test for overall effectz = -4.35, p < 0.001
I²82.5%represents considerable heterogeneity (75-100%)
Cochran's Q28.51 (df = 5, p < 0.001)
τ² (between-study variance)0.1981τ = 0.4450 on the log scale
95% prediction interval0.09 to 1.49Where the true effect in a new study is likely to lie

With fewer than 10 studies, heterogeneity statistics are imprecise; interpret I² with its context.

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How to Use the I² Heterogeneity Calculator

  1. 1

    Choose the effect measure

    Ratio (odds, risk or hazard) or difference (mean difference or SMD).

  2. 2

    Enter your studies

    2×2 counts or each estimate with its 95% confidence interval.

  3. 3

    Read the results

    I², Q, τ² and the prediction interval, with interpretation.

What does I² mean in a meta-analysis?

I² describes the percentage of variability in effect estimates that is due to heterogeneity rather than chance. Cochran's Q tests whether the studies share a common effect, and τ² estimates how much the true effects vary.

The Cochrane Handbook gives overlapping, rough guides: 0% to 40% might not be important, 30% to 60% may represent moderate heterogeneity, 50% to 90% substantial, and 75% to 100% considerable. The importance of I² also depends on the size and direction of the effects.

How it is calculated

Sources

I² Heterogeneity Calculator: FAQ

What is a good I² value?

There is no universal cut-off. Cochrane's rough guide treats 0-40% as possibly unimportant and 75-100% as considerable, but always consider the size and direction of effects.

What is the difference between I² and τ²?

I² is a percentage of variability due to heterogeneity; τ² is the actual variance of true effects on the analysis scale. τ² does not depend on study size in the same way I² does.

Why report a prediction interval?

It shows the range in which the true effect of a new study is likely to fall, which is often wider than the confidence interval when heterogeneity is present.

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