Data Tools & Calculators

Enter confusion matrix counts to compute precision, recall, F1, specificity, MCC, likelihood ratios, and more.

Compute absolute difference and relative (percent) change between two values.

Calculate the statistical power of your experiment to determine the probability of detecting an effect if one exists.

Calculate Cohen's d, odds ratios, and other effect size measures to quantify the magnitude of differences between groups.

Calculate confidence intervals for means and proportions to estimate population parameters with specified confidence levels.

Perform one-sample, two-sample, or paired t-tests to compare means and determine statistical significance.

Compute the z-score and percentile of a value assuming a normal distribution.

Upload panel data (long or wide), select treated units and intervention date, and visualize synthetic control results with placebo-based confidence intervals.

Enter nodes and probability edges (via JSON) to visualize a state machine and validate its correctness.

Enter A/B test metrics and sample sizes to compute confidence intervals, p-values, and highlight statistically significant results.

Calculate the required sample size for your A/B test based on baseline conversion rate, minimum detectable effect, and statistical power.

Estimate how long your A/B test should run to reach statistical significance based on your traffic and expected effect size.