All Calculators

Find the arithmetic average of a set of numbers.

Quantify the typical spread of your data around its mean.

Find the middle value of a dataset, robust to extreme values.

Find the spread between the smallest and largest values in a dataset.

Find the most frequently occurring value or values in a dataset.

Combine values with explicit weights, rather than treating each equally.

Compare relative variability across datasets measured on ratio scales.

Report the minimum, quartiles, median, and maximum in one summary.

Quantify squared spread with a stated sample or population denominator.

Find the value or rank at any percentile of a dataset.

Summarize central spread and potential outlier fences with quartiles.

Report the average absolute distance from a chosen center.

Summarize multiplicative growth factors, not additive levels.

Average positive rates over equal distances or quantities.

Standardize an observation relative to a stated mean and SD.

Get a full transparent profile of one numeric variable in a single view.

Convert normal-distribution values to areas, or areas to values.

Find the exact probability of a specific number of successes in n trials.

Calculate a discrete expected value and its variability.

Count unordered selections or ordered arrangements from a set.

Apply the addition, multiplication, and complement rules to named events.

Find P(A|B) directly from a joint probability and a conditioning event.

Update a prior probability with test sensitivity and specificity evidence.

Validate a custom PMF and summarize its mean, variance, and SD.

Find the value at a given percentile of a normal distribution.

Find the probability of at most or at least a given number of successes.

Model rare, independent event counts over a fixed exposure.

Look up Student's t distribution's CDF or a critical value directly.

Look up the chi-square distribution's CDF or upper-tail probability directly.

Look up the F distribution's CDF or upper-tail probability directly.

Estimate a population mean from a sample, with a stated margin of uncertainty.

Estimate a binomial proportion with a defensible interval, not a bare percentage.

Find the sampling precision for a proportion given a sample size.

Estimate the gap between two independent group means with a Welch interval.

Estimate the mean change between matched pairs with a t interval.

Give a Fisher z interval around a Pearson correlation, not just a point estimate.

Route to the right confidence interval method for your design, not one generic answer.

Estimate the absolute risk difference between two independent proportions.

Estimate association from a 2 by 2 table with a log-odds Wald interval.

Estimate relative risk from cohort-style exposure and event counts.

Compare the means of two independent groups using Welch’s t-test by default.

Test whether two categorical variables are associated in a contingency table.

Compare two measurements taken on the same subjects or matched pairs.

Compare two independent proportions, such as two conversion or event rates.

Compare a single sample mean against a fixed benchmark value.

Test whether two or more groups show evidence of unequal variance.

Test a single observed proportion against a fixed benchmark rate.

Test whether observed category counts match a stated expected distribution.

Convert a statistic and named null distribution into a tail probability.

Test a binomial proportion exactly, with no normal-approximation assumption.

Test 2 by 2 association exactly, safe for sparse expected counts.

Compare paired binary outcomes using only the discordant pairs.

Measure the linear association between two numeric variables.

Measure monotonic association between two variables using ranks.

Summarize nominal association magnitude in any contingency table.

Measure ordinal concordance between two variables, robust to ties.

Fit and interpret a simple linear predictive relationship between two variables.

Compare model fit accounting for the number of predictors used.

Summarize first-order autocorrelation in ordered regression residuals.

Give prediction uncertainty for one new individual outcome.

Give mean-response uncertainty at a specific x value.

Compare means across three or more independent groups at once.

Compare group means without assuming equal variance across groups.

Quantify the proportion of variation explained by group membership.

Provide a less biased ANOVA effect-size estimate than eta squared.

Compare two independent groups without assuming a normal distribution.

Compare three or more independent groups using ranks, not raw values.

Compare matched pairs without assuming a normal distribution of differences.

Test direction-only evidence from paired data or a single median comparison.

Plan the sample size needed to estimate a proportion to a target precision.

Plan the sample size needed to estimate a mean to a target precision.

Plan an independent two-group mean comparison before collecting data.

Find the smallest effect a planned sample size can reliably detect.

Estimate the internal consistency of a multi-item scale or survey.

Estimate internal consistency by correlating two halves of a scale.

View the shape of a numeric distribution with a documented bin rule.

Compare distributions and flag potential outliers using quartile fences.

Describe a distribution's asymmetry and tail shape numerically.

Flag candidate outliers with an IQR fence or a robust modified z-score.

Formally test whether the single most extreme value is a statistical outlier.