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.

Generate reproducible pseudo-random values from a selected distribution.

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.

Estimate uncertainty for a statistic without a closed-form formula.

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.

Assess sample evidence against a normal model with the most widely used test.

Assess distributional fit with extra sensitivity to tail departures.

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.

Associate a binary group variable with a numeric outcome.

Summarize association strength and direction in a 2 by 2 table.

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.

Fit adjusted linear associations across two or more predictors at once.

Model a binary outcome from one or more predictors via maximum likelihood.

Fit a declared curved (quadratic or cubic) relationship between two variables.

Inspect fitted-model residual patterns, leverage, and influence flags.

Quantify how much predictor collinearity inflates coefficient variance.

Flag observations disproportionately influential to an OLS fit.

Compare two properly nested OLS models with a partial F-test.

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.

Adjust a declared family of p-values or alpha for multiple comparisons.

Compare three or more related measurements on the same subjects.

Estimate two factor effects and their interaction in a balanced design.

Compare adjusted group means after controlling for a covariate.

Make family-wise adjusted all-pair comparisons after a one-way ANOVA.

Make pairwise comparisons after group variances are found unequal.

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.

Compare three or more related conditions using ranks within each block.

Compare group medians with a low-power, low-assumption test.

Test for an ordered trend across three or more independent groups.

Make adjusted pairwise rank comparisons after a Kruskal-Wallis test.

Report a Mann-Whitney effect size directly from U and group sizes.

Report probability-based ordinal dominance between two groups.

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.

Route to the sample-size planner built for your specific design.

Plan a matched-pairs study using the variability of the differences.

Plan an independent two-proportion comparison before collecting data.

Plan detection or precision for a target Pearson correlation.

Plan a balanced k-group ANOVA using the exact noncentral F relationship.

Find the achieved or prospective power for a one-sample, paired, or independent design.

Find the achieved power for a one- or two-proportion design.

Find the achieved power for a balanced one-way ANOVA from Cohen's f and n.

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

Estimate internal consistency by correlating two halves of a scale.

Quantify chance-corrected agreement between two raters on categories.

Quantify agreement on ordered categories, penalizing larger disagreements more.

Quantify rater or measurement reliability for an explicit ICC model.

Assess agreement and bias between two measurement methods.

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.

Inspect paired observations for pattern, nonlinearity, and outliers.

Compare a sample against a normal distribution using ordered quantiles.

Summarize missingness by column and by row without imputing anything.

Screen pairwise linear associations among several numeric variables at once.

Check linear-model residual structure for patterns worth investigating.

Combine skewness, kurtosis, and formal tests into one normality evidence summary.

Resample a declared statistic to explore its sampling variability directly.

Test a statistic with an exchangeability-based null, not a distributional assumption.

Estimate a probability from a transparent, reproducible random model.

Draw a reproducible random sample of rows, with or without replacement.

Show how a sample statistic varies across many repeated samples.