Fit a binary logistic regression in the browser by iteratively reweighted least squares. Paste an outcome column plus numeric predictors and get coefficients, odds ratios with 95% CI, Wald p-values, the likelihood-ratio test, McFadden R-squared, and AIC. Detects separation. Fit matches statsmodels; R, Python, and APA export.
Features
IRLS maximum-likelihood fit
Odds ratios with 95% CI
Multiple predictors
Likelihood-ratio test + McFadden R²
Separation detection
APA Word + CSV export
R + Python code
Paste or upload a dataset and get a publication-ready baseline characteristics table (Table 1). Detects continuous vs categorical variables, summarizes each by group as mean (SD), median [IQR], or n (%), runs the correct group-comparison test (t-test, ANOVA, Mann-Whitney, Kruskal-Wallis, chi-square, or Fisher's exact), and exports an APA-formatted Word table.
Features
Auto continuous vs categorical detection
Mean (SD), median [IQR], n (%)
Auto test selection + p-values
Skew-aware median switch
APA Word + Excel + CSV export
CSV / Excel import
Answer a few questions about your outcome, groups, and design and get the right statistical test with its assumptions, the non-parametric or exact alternative, and a direct link to the free calculator that runs it. Covers t-tests, ANOVA, chi-square, Fisher's exact, correlation, regression, McNemar, kappa, ICC, and Cronbach's alpha.
Features
Guided decision tree
Assumption checklist per test
Non-parametric + exact alternatives
Direct links into 15+ calculators
Paired vs independent logic
Small-sample routing to exact tests
Run the McNemar test on paired binary data with asymptotic chi-square, Yates continuity correction, exact binomial and mid-p p-values, Newcombe Method 10 95% CI for the paired difference in proportions, conditional odds ratio with log-Wald CI, Cohen's kappa, plus McNemar-Bowker symmetry and Stuart-Maxwell marginal homogeneity tests for k × k tables. Reproducible R, Python, and APA output.
Features
Asymptotic + Yates + exact + mid-p p-values
Newcombe Method 10 paired RD 95% CI
Conditional OR with log-Wald CI
Cohen's kappa for paired agreement
McNemar-Bowker symmetry for k × k
Stuart-Maxwell marginal homogeneity
R + Python + APA + CSV export
Compute Cronbach's α (raw and standardised) for scale reliability with Feldt and Bonett 95% confidence intervals, McDonald's ω, Guttman's λ₆, Spearman-Brown split-half reliability, item-total correlations, and α-if-item-deleted. Three input modes (raw data, correlation matrix, or summary statistics) with reproducible R, Python, and APA output.
Features
Raw and standardised α
Feldt + Bonett 95% CIs
McDonald's ω + Guttman's λ₆
Item-total r + α-if-deleted
Spearman-Brown split-half
R + Python + APA + CSV export
Run the Kruskal-Wallis H test (non-parametric one-way ANOVA) on three or more independent groups with ties-corrected H, chi-square p-value, epsilon-squared and eta-squared effect sizes, and Dunn's post-hoc with Bonferroni / Holm / Sidak adjustment. Also covers the Friedman test for repeated measures and the Jonckheere-Terpstra trend test. Reproducible R, Python, and APA output.
Features
Ties-corrected H statistic
Epsilon² + eta² + Kendall's W effect sizes
Dunn's post-hoc (Bonferroni / Holm / Sidak)
Friedman repeated-measures mode
Jonckheere-Terpstra trend test
R + Python + APA + CSV export
Run the Mann-Whitney U (Wilcoxon rank-sum) test on two independent groups with exact and asymptotic ties-corrected p-values, rank-biserial r, Cliff's delta, common-language effect size, and the Hodges-Lehmann median with 95 percent confidence interval. Also covers the Wilcoxon signed-rank test for paired data and the Brunner-Munzel test under unequal variances. Reproducible R, Python, and APA output.
Features
Exact + asymptotic p-values
Ties-corrected variance + continuity correction
Rank-biserial r, Cliff's delta, CLES
Hodges-Lehmann median + 95% CI
Wilcoxon signed-rank for paired data
Brunner-Munzel for heteroscedastic shapes
R + Python + APA + CSV export
Run Fisher's exact test on 2 × 2 and r × c contingency tables with two-sided and one-sided exact p-values, mid-p adjustment, odds ratio with exact 95% confidence interval, and Barnard's and Boschloo's unconditional exact tests. Reproducible R, Python, and APA output for small-sample categorical data.
Features
2×2 conditional Fisher + mid-p
One-sided and two-sided exact p
Odds ratio + risk ratio + ARD + NNT
Barnard + Boschloo unconditional exact
Fisher-Freeman-Halton Monte Carlo r×c
R + Python + APA export
Run one-way, two-way factorial, and repeated-measures analysis of variance in your browser. Returns the full sources-of-variance table, F-statistic, p-value, eta-squared, omega-squared, partial eta-squared, Cohen's f, Welch's correction for unequal variances, Bonferroni / Holm / Sidak post-hoc pairwise tests, and Greenhouse-Geisser plus Huynh-Feldt sphericity-corrected p-values with reproducible R, Python, and APA output.
Features
One-way + two-way + repeated measures
Welch's ANOVA for unequal variances
Eta² / partial η² / omega² / Cohen's f
Bonferroni + Holm + Sidak pairwise
Greenhouse-Geisser + Huynh-Feldt sphericity correction
R + Python + APA + CSV export
Compute P(X = k), P(X ≤ k), P(X ≥ k), and between-bounds probabilities from a binomial distribution. Run an exact one-sample binomial test and build Clopper-Pearson, Wilson, Agresti-Coull, or Wald 95 percent confidence intervals for a proportion, with reproducible R, Python, and APA output.
Features
PMF, CDF, and survival probabilities
Six probability kinds (=, <, ≤, >, ≥, between)
Exact one-sample binomial test
Clopper-Pearson, Wilson, Agresti-Coull, Wald CIs
Full PMF / CDF table for k = 0..n
R + Python + APA export
Compute sample variance, population variance, standard deviation, coefficient of variation, pooled variance, and the chi-square 95 percent confidence interval from raw data or summary statistics. Includes an F-test for equal variances and reproducible R, Python, and APA output.
Features
Sample + population variance
Coefficient of variation
Pooled variance for two samples
Chi-square 95% CI for σ²
F-test for equal variances
R + Python + APA export
Convert a raw value or sample mean into a z-score, look up the corresponding percentile and p-value, and translate between z and IQ, T, SAT, GRE, or any custom scale. Includes a built-in one-sample z-test and reproducible R, Python, and APA output.
Features
Raw value + sample mean modes
z ↔ percentile (inverse normal)
One-sample z-test with SE
IQ / T / SAT / GRE / stanine conversion
R + Python + APA export
Convert a z, t, chi-square, F, or Pearson correlation test statistic into a p-value with two-tailed or one-tailed options. Returns critical values at α = 0.05, 0.01, and 0.001 alongside reproducible R, Python, and APA-formatted output.
Features
z, t, χ², F, r distributions
Two-tailed and one-tailed
Critical values at α = 0.05/0.01/0.001
APA results sentence
R + Python export
Back-calculate confidence intervals from reported p-values, t-statistics, chi-square values, or z-scores. Reconstruct CIs and send results to forest plot, funnel plot, or heterogeneity tools via the analysis pipeline.
Features
From p/t/χ²/z values
CI reconstruction
Pipeline send-to
Excel/CSV export
Compute 80, 90, 95, or 99 percent confidence intervals for means, proportions, mean differences, proportion differences, odds ratios, and risk ratios. Wilson, Newcombe, and log-Wald methods with reproducible R code export.
Features
Mean / proportion / OR / RR
Wilson + Newcombe methods
Continuity correction for sparse 2x2
R code export
Run simple and multiple ordinary least squares regression in your browser. Returns coefficients with standard errors, t-statistics, p-values, and 95 percent confidence intervals alongside R squared, adjusted R squared, F-test, RMSE, residual diagnostics, and R, Python, or APA reporting export.
Features
Simple + multiple OLS
Coefficient table with 95% CI
Residual + scatter diagnostics
R / Python / APA code export
Independent-samples t-test from summary statistics or raw data. Welch and Student variants, two- and one-tailed alternatives. Returns t, df, p-value, confidence interval for the mean difference, Cohen's d, Hedges' g, and R, Python, and APA write-up export.
Features
Welch + Student variants
Summary stats or raw data
t, df, p, CI, Cohen's d, Hedges' g
R / Python / APA export
Guide extraction of time-to-event data from Kaplan-Meier curves. Enter time points and at-risk numbers to estimate hazard ratios and median survival times.
Features
KM data extraction
HR estimation
Tierney/Parmar methods
Export-ready
Compute Pearson r, Spearman ρ, and Kendall τ-b from paired raw data or from a known r and n. Returns r², t-statistic, p-value, and Fisher z 95% confidence interval with R, Python, and APA-formatted output.
Features
Pearson + Spearman + Kendall τ-b
Raw paired data or r and n
r² + t + p + 95% Fisher z CI
Two- and one-tailed tests
R / Python / APA export
Convert between Pearson r, r², Cohen’s d, odds ratios, and other effect metrics with the exact formulas displayed for each conversion.
Features
Bidirectional conversion
Formula display
6 effect metrics
Copy results
Convert between standard deviation, standard error, confidence intervals, and sample size. Provide any two known values to derive the rest.
Features
SD/SE/CI/N conversion
Bidirectional
Instant results
Copy to clipboard
Estimate mean and standard deviation from median, interquartile range, and sample size using validated methods by Wan et al. (2014), Luo et al. (2018), McGrath et al. (2020), and Hozo et al. (2005). Batch-process multiple studies and send results to the effect size calculator.
Features
Wan/Luo/Hozo methods
Batch CSV/Excel import
Pipeline to effect size
Method comparison
Analyze contingency tables with Pearson’s chi-square test, Yates’ continuity correction, expected counts, Cramér’s V, and phi coefficient. For exact testing on sparse cells switch to the Fisher’s exact test calculator.
Features
Pearson chi-square + Yates
Cramér’s V & phi
Expected counts
Effect sizes
Compute required sample sizes for t-tests, ANOVA, chi-square tests, correlations, and regression. Solve for sample size, power, or effect size given the other parameters.
Features
5 test types
Solve any parameter
Power curves
Preset conventions
Compute intraclass correlation coefficients from rater data. Supports all six ICC forms: ICC(1,1), ICC(2,1), ICC(3,1), ICC(1,k), ICC(2,k), and ICC(3,k).
Features
All 6 ICC forms
Rater data matrix
CI estimation
Interpretation guide