Sample Size Calculators

Estimate a population mean within a specified margin of error.

Compare one sample mean against a known or hypothesised value.

Compare means between two independent groups.

Compare two related measurements, such as before-and-after values.

Estimate a mean from a small, fixed population.

Estimate a single population proportion with a desired margin of error.

Compare one observed proportion against a hypothesised value.

Compare proportions between two independent groups.

Compare paired binary outcomes.

Plan an omnibus Pearson chi-square comparison across three or more independent groups.

Plan approximate confidence-interval precision for one continuous population median.

Plan an exact one-sample sign test of a hypothesised population median.

Plan two independent groups using Noether’s large-sample Mann–Whitney approximation.

Plan paired observations using an exact sign test or a distribution-adjusted Wilcoxon signed-rank approximation.

Kruskal–Wallis omnibus planning under a Normal common-SD location-shift model.

Estimate a population correlation coefficient with desired precision.

Test whether a correlation differs from a specified value.

Compare correlation coefficients from two independent groups.

Plan a same-participant comparison of two Pearson correlations sharing one variable.

Plan a same-participant comparison of two Pearson correlations with no variables in common.

Plan a study to test an association using simple linear regression.

Plan a study powered to test one prespecified adjusted regression coefficient.

Plan an omnibus test of whether a prespecified multiple linear regression model explains outcome variance.

Plan a nested-model test of the R² increase contributed by a prespecified predictor block.

Plan a simple logistic-regression test for one continuous or binary predictor.

Estimate internal consistency reliability with desired precision.

Test whether Cronbach alpha meets a target reliability value.

Estimate intraclass correlation reliability with desired confidence-interval width.

Test whether ICC exceeds a minimum acceptable value.

Estimate two-rater kappa agreement with desired precision.

Estimate diagnostic-test sensitivity and specificity with a binary reference standard.

Test one diagnostic-test sensitivity against a fixed benchmark value.

Test one diagnostic-test specificity against a fixed benchmark value.

Estimate one ROC area under the curve with equal reference-standard-positive and reference-standard-negative groups.

Test whether a diagnostic test's anticipated ROC AUC is better than a prespecified minimum acceptable benchmark.

Plan approximate confidence-interval precision for a Kaplan–Meier median survival estimate.

Plan precision for a Kaplan–Meier survival probability at one landmark time.

Plan a single-arm, one-sided test of a Kaplan–Meier survival probability at a prespecified landmark time.

Plan events and participants for a two-group log-rank comparison using median survival, landmark survival, or direct hazard-ratio assumptions.

Plan target events and participants to test one binary predictor in a Cox proportional-hazards model.

Plan a two-arm superiority trial with a continuous endpoint and a prespecified mean-difference margin.

Plan a two-arm non-inferiority trial with a continuous endpoint and a prespecified margin.

Plan a two-arm continuous-outcome equivalence trial with symmetric mean-difference limits.

Plan a two-arm superiority trial for an absolute risk-difference endpoint.

Plan a two-arm non-inferiority trial for an absolute risk-difference endpoint.

Plan a one-sided superiority comparison of two cluster-randomized arms with a continuous endpoint.

Plan a cluster-randomized non-inferiority trial with a continuous endpoint and prespecified margin.

Plan a cluster-randomized equivalence trial with continuous-outcome equivalence limits.

Plan a one-sided superiority comparison of two cluster-randomized arms with a binary endpoint.

Plan clusters and participants for a binary outcome using a justified absolute risk-difference non-inferiority margin.

Plan a 2×2 AB/BA crossover superiority trial with a continuous outcome.

Plan a 2×2 AB/BA crossover non-inferiority trial with a continuous outcome.

Plan a 2×2 AB/BA crossover equivalence trial with a continuous outcome.

Plan a 2×2 AB/BA crossover superiority trial with a binary outcome.

Plan a 2×2 AB/BA crossover non-inferiority trial with a binary outcome.

Plan objective-specific pilot requirements for feasibility proportions, rates, progression criteria, or detecting important problems.

Plan a two-group longitudinal linear mixed model powered for a difference in mean linear slopes.

Plan a two-group linear mixed model powered for an average marginal mean difference across follow-up visits.

Plan a two-group linear mixed model powered for a marginal mean difference at one prespecified follow-up.

Plan an omnibus joint test of whether two longitudinal mean profiles differ across categorical follow-up visits.

Plan a two-group parallel trial using an MMRM superiority comparison at a prespecified follow-up visit.

Plan a two-group parallel trial using an MMRM non-inferiority comparison at a prespecified follow-up visit.

Plan a two-group parallel trial using MMRM and prespecified equivalence limits at a selected follow-up visit.

Plan a two-group parallel trial for a prespecified equal-weight average MMRM treatment contrast across selected follow-up visits.

Plan a population-averaged GEE joint test of whether two marginal mean profiles differ across categorical follow-up visits.

Plan a population-averaged GEE test of a two-group marginal mean difference at one prespecified follow-up.

Plan a population-averaged GEE test of an equally weighted mean difference across follow-up visits.

Plan a logistic GEE test of a common population-averaged odds ratio across repeated binary outcomes.

Plan a population-averaged GEE test of a difference between two linear longitudinal trends.