Two-Proportion z-Test
Computes the two-proportion z-test statistic, which compares the success rates of two independent groups — the backbone of any A/B test. For example: variant A converted 45 of 100 and B converted 30 of 100; is the difference real or noise? The statistic uses the pooled proportion of the two groups to estimate the standard error under the hypothesis that the proportions are equal. Enter the successes and the total of each of the two groups.
Resultado
—
Two-Proportion z-Test
Computes the two-proportion z-test statistic, which compares the success rates of two independent groups — the backbone of any A/B test. For example: variant A converted 45 of 100 and B converted 30 of 100; is the difference real or noise? The statistic uses the pooled proportion of the two groups to estimate the standard error under the hypothesis that the proportions are equal. Enter the successes and the total of each of the two groups.
Is the difference between the two rates real?
This is the test behind practically every A/B experiment. You have two versions, two campaigns, two treatments, and you measure the success rate of each. Variant A converted more than B, but is the difference a genuine effect or just sampling fluctuation? The two-proportion z-test separates the signal from the noise.
Under the hypothesis that the two proportions are equal, the best estimate of the common proportion comes from pooling the successes and totals of both groups into a combined proportion. That pooled proportion feeds the calculation of the standard error of the difference, and the z statistic measures how many standard errors separate the two observed rates. The larger the z in magnitude, the harder it is to chalk the difference up to chance.
Enter the successes and the total of each group. The tool returns the z statistic, from which the p-value of the two-sided or one-sided test follows. As with any normal-based proportion test, the groups must be large enough; with few successes, consider an exact test such as Fisher's.
Related Tools
CI for the Difference of Two Proportions
Computes the confidence interval for the difference between two proportions, using the Wald method. It's the companion to the two-proportion z-test and the basis of any A/B test reading: beyond saying whether the difference is significant, it shows the plausible range for its real size. If the interval doesn't contain zero, there's a significant difference. For example: variant A converted 45 of 100 and B, 30 of 100 — what's the interval for the difference? Enter the successes and the total of each group and the confidence level.
CI for Difference of Two Means (t)
Computes the confidence interval for the difference between the means of two independent groups, using the t distribution with pooled variances. It's the natural companion to the two-sample t-test: instead of only saying whether the difference is significant, it shows the plausible range for the real size of that difference. If the interval doesn't contain zero, there's a significant difference at the chosen level. Enter the two samples of values and the confidence level.
Paired t-Test
Computes the paired t-test, used when you measure the same subjects twice — before and after a treatment, or by two methods. It works with the differences of each pair and tests whether the mean difference is compatible with zero. By removing the variability between subjects, it's usually far more powerful than comparing two independent groups. The tool returns the t statistic, the degrees of freedom and the two-sided p-value. Enter the two paired series.
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.