Statistics & Probability calculators
This category spans descriptive statistics, probability and distributions, hypothesis tests, confidence intervals and sample size planning, correlation and regression, and process capability for quality control. Each calculator reports the statistic along with the intermediate values and, for tests, the p-value and how to read it.
37 tools · Updated October 21, 2026
Which one do I need?
Describing a data set
Start with center and spread. The mean and median describe the typical value, and the standard deviation and interquartile range describe how far values stray from it. For skewed data or data with outliers, the median and IQR are more robust, and the Box Plot Calculator shows them visually along with any outliers flagged by the 1.5 × IQR rule.
Sample and population formulas differ. The sample standard deviation divides by n − 1 rather than n, which corrects for the tendency of a sample to underestimate the true spread. Use the population version only when your data includes every member of the group you care about.
Choosing a hypothesis test
The right test depends on the type of data and the number of groups. For comparing means of two groups, use a t-test; for three or more, one-way ANOVA, or two-way ANOVA when there are two factors. For counts in categories, use the chi-square test. When data is clearly non-normal or ordinal, the Mann-Whitney U and Kruskal-Wallis tests compare groups using ranks instead of raw values.
A p-value is the probability of seeing a result at least this extreme if the null hypothesis were true. It is not the probability that the null hypothesis is true. Report an effect size alongside it, because with a large enough sample even a trivial difference becomes statistically significant. The Effect Size Calculator gives Cohen's d, eta squared and r.
Planning studies and modeling relationships
Sample size and power should be settled before data collection. Power is the chance of detecting an effect of a given size if it really exists, and 80% is a common target. The Power Analysis Calculator links sample size, effect size, significance level and power, and the Sample Size Calculator handles survey margins of error.
Correlation measures how strongly two variables move together, from −1 to 1; Spearman's version works on ranks and suits monotonic but non-linear patterns. Regression goes further and predicts one variable from others. Use linear regression for one predictor, multiple regression for several, and logistic regression when the outcome is yes or no.