Levene's Test (Equality of Variances)
Computes Levene's test, which checks whether several groups share the same variance — the homogeneity assumption that ANOVA and the t-test rely on. Instead of working with the original values, it transforms each data point into the absolute deviation from its group mean and runs an ANOVA on those deviations, gauging whether the spread differs across groups. It's more robust to non-normality than Bartlett's test. Enter the groups: values by comma, groups by semicolon.
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Levene's Test (Equality of Variances)
Computes Levene's test, which checks whether several groups share the same variance — the homogeneity assumption that ANOVA and the t-test rely on. Instead of working with the original values, it transforms each data point into the absolute deviation from its group mean and runs an ANOVA on those deviations, gauging whether the spread differs across groups. It's more robust to non-normality than Bartlett's test. Enter the groups: values by comma, groups by semicolon.
Before you run ANOVA, check the variances
Both the t-test and ANOVA carry fine print in the contract: they assume the groups being compared have similar spreads. When one group is far more scattered than another, the results lose reliability. Levene's test exists to audit that assumption before you trust the main conclusion.
The trick is elegant. Instead of comparing the values directly, Levene measures how far each point sits from its group mean, in absolute terms, and runs an ANOVA on those distances. If the spread is similar across groups, those absolute deviations will have similar means and the W statistic stays small. If one group is clearly more dispersed, W grows.
Enter the groups with values separated by commas and groups by semicolons. The tool returns W and the degrees of freedom. Because Levene leans on deviations rather than raw values, it tolerates data that stray from normality well, which is why it's the test of choice in practice when the shape of the distribution is in doubt.
Related Tools
Bartlett's Test (Equality of Variances)
Computes Bartlett's test, the classic test for checking whether several groups share the same variance. It compares the pooled variance of all groups against the individual variances through a statistic that approximately follows a chi-square distribution, with a bias correction that improves the approximation in small samples. It's more powerful than Levene's test when the data are genuinely normal, but more sensitive to departures from normality. Enter the groups: values by comma, groups by semicolon.
Brown-Forsythe Test (Variances)
Computes the Brown-Forsythe test, a robust version of Levene's test for checking whether several groups share the same variance. The difference lies in one decisive detail: instead of measuring how far each point sits from the group mean, it uses the distance from the median. Because the median resists extreme values and skewed distributions, the test becomes much more reliable when the data aren't normal. Enter the groups: values by comma, groups by semicolon.
F-Test for Equality of Variances
Computes the F-test for comparing the variances of two independent groups, checking whether they have the same spread. It's a common step before choosing between the classic t-test (which assumes equal variances) and Welch's (which doesn't). The statistic is the ratio of the two sample variances, and the F distribution, with its two degrees of freedom, provides the p-value. The tool takes the two samples and computes F, the degrees of freedom and the two-sided p-value. Enter the two groups of values.
The results provided by this tool are for general informational and educational purposes only and do not constitute professional, financial, medical, legal, tax or accounting advice. Always confirm important decisions with a qualified professional and official sources.