Statistical assumptions checker
Paste one numeric sample to inspect normality, shape and possible outliers in one report.
Normal QQ plot
Points close to the reference line are consistent with a normal shape.
Diagnostic tests
| Test | Statistic | p-value |
|---|---|---|
| Fill in the fields above — the result is computed on this page. | ||
How do I use this page?
- Paste the observations.
- Inspect the QQ plot and outliers.
- Use the test selector if assumptions are doubtful.
What this page does not do: The Shapiro result is a documented Shapiro–Francia large-sample approximation. Results can be unstable for very small samples and overly sensitive for large samples.
Further reading
Theory — what does a statistical assumptions checker verify?
The statistical assumptions checker inspects normality, distribution shape and possible outliers in a single report, based on one pasted numeric sample. The report combines a normal-scores approximation, the Anderson–Darling test, a quantile-quantile plot, skewness, kurtosis and the interquartile-range rule. This check supports choosing an appropriate statistical model, but it does not replace looking at the data or evaluating how the study was designed. A p-value above 0.05 does not prove normality — it only means there is no strong evidence against that assumption in this sample.
Frequently asked questions
Does p > 0.05 prove normality?
Should every outlier be removed?
See also
A/B test calculator
Compare two proportions in an A/B test. Calculate conversion rates, difference, uplift, z score, p value and c
A/B test planning — sample size, MDE and duration
Plan a two-proportion experiment, estimate detectable effect and traffic duration.
ACF and PACF autocorrelation calculator
Calculate ACF and PACF across time-series lags. Review the chart, reference bounds and coefficient table.