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Welch's t-test

Computes the Welch's t-test statistic, the two-sample t-test version that does NOT assume equal variances. Unlike the classic Student's t-test, it doesn't pool the variances into a single value, which makes it more reliable when the two groups have different spreads — the default recommendation in modern statistics. The degrees of freedom come from the Welch-Satterthwaite equation, usually fractional. Enter the mean, standard deviation and size of each of the two groups.

Resultado

Welch's t-test

Computes the Welch's t-test statistic, the two-sample t-test version that does NOT assume equal variances. Unlike the classic Student's t-test, it doesn't pool the variances into a single value, which makes it more reliable when the two groups have different spreads — the default recommendation in modern statistics. The degrees of freedom come from the Welch-Satterthwaite equation, usually fractional. Enter the mean, standard deviation and size of each of the two groups.

The t-test that doesn't trust equal variances

The classic Student's t-test carries a fragile assumption: that the two groups compared have the same variance. When that doesn't hold, and it rarely does in practice, the test loses reliability. Welch's t-test fixes this so robustly that many statisticians argue for using it always, by default, instead of the classic one.

The difference is in how it treats the variances. Instead of pooling them into a single value, Welch keeps each one separate in computing the standard error. The price is that the degrees of freedom stop being a round number and come from the Welch-Satterthwaite equation, almost always fractional, reflecting the imbalance between the groups.

Enter the mean, standard deviation and size of each of the two groups. The tool returns the t-statistic and the Welch degrees of freedom. With those two numbers you consult the t-distribution to get the p-value and decide whether the difference between the means is statistically significant.

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Mann-Whitney U Test

Computes the Mann-Whitney U test, the non-parametric alternative to the t-test for comparing two independent groups. Instead of comparing means, it compares the ranks of the combined data, which makes it valid even when the data don't follow a normal distribution or are ordinal. The tool computes the U statistic and the normal approximation z, with a tie correction. Enter the two groups of observations, separated by commas.

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One-Proportion z-Test

Computes the one-proportion z-test statistic, used to check whether the proportion observed in a sample differs from a hypothesized value. For example: out of 100 voters, 45 approve a measure — is that compatible with 50% in the population? The statistic divides the difference between the observed and hypothesized proportions by the standard error computed under the null hypothesis. It applies when the sample size is large enough for the normal approximation. Enter the successes, the total and the hypothesized proportion.

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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.

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.