ANOVA One Way Calculator
Run one-way ANOVA summary statistics for multiple groups.
Compute sums of squares, mean squares, and F statistic for one-factor group comparisons.
One page. One job. Done.
Mini Tools Collection
ToolPatch is a growing collection of focused utilities. Each page handles one task with clear inputs, fast results, and practical outputs.
Run one-way ANOVA summary statistics for multiple groups.
Compute sums of squares, mean squares, and F statistic for one-factor group comparisons.
Compute posterior probability from prior, sensitivity, and false positive rate.
Useful for medical test interpretation, risk scoring, and signal-detection decisions.
Estimate confidence intervals from mean, standard deviation, and sample size.
Calculate margin of error and confidence bounds for common confidence levels. Great for analysis reports, A/B test summaries, and classroom statistics.
Convert confidence levels to z critical values and margin of error.
Get z* for common and custom confidence levels and estimate margin of error with sigma and n.
Compute probability between two bounds in a normal distribution.
Converts bounds to z-scores and calculates probability mass between them.
Compute one-sample t-statistic from sample values and hypothesized mean.
Enter sample values and compare against a target mean using the one-sample t-test setup.
Calculate Pearson r and R² from paired X and Y datasets.
Quantify linear relationship strength between two numeric variables.
Calculate exact and cumulative Poisson event probabilities.
Estimate event-count probabilities over fixed intervals when events are rare and independent.
Compute RR, OR, and confidence intervals from a 2x2 contingency table.
Analyze exposure-outcome association with relative risk, odds ratio, and 95% confidence bounds.
Estimate required sample size from confidence level, margin error, and proportion.
Supports infinite-population and finite-population adjusted sample size estimates.
Calculate chi-square goodness-of-fit statistic from observed vs expected counts.
Useful for categorical fit checks and introductory hypothesis testing workflows.
Compare sample variances with F statistic and degrees of freedom.
Compute two-sample variance comparison metrics to support homogeneity checks.
Find geometric mean for positive-number datasets.
Useful for growth rates, index performance, and multiplicative process summaries.
Calculate harmonic mean for rates and ratios.
Best for averaging speeds, price multiples, and other ratio-based metrics.
Compute rolling averages over a configurable window.
Useful for smoothing noisy series and spotting trend direction.
Find percentile rank of a target value within a dataset.
Helpful for score interpretation, benchmarking, and education metrics.