Q-Q Plot 2D: Testing Normality with a Quantile-Quantile Scatter

A quantile-quantile plot is the workhorse diagnostic statisticians reach for before trusting any method that assumes normal data — t-tests, ANOVA, linear-regression residuals, control charts. This simulator draws a fresh random sample from one of five populations (normal, right-skewed, heavy-tailed, uniform or a bimodal mixture), sorts it, and plots each value against the quantile a perfect normal sample would produce at that rank. Each point is colored by its residual from the ideal y = x line, so skew, fat tails and thin tails visibly bend the scatter away from the diagonal instead of hiding in a static picture — drag to pan and scroll to zoom into any region of the plot. Live readouts track the R² of the fit, sample skewness and excess kurtosis as you resample or change the population and sample size.