Who enters? Who gets treatment?
Change recruitment and treatment assignment to separate association, causal effects among volunteers, and population generalization.
The simplest way to understand a mathematical claim is sometimes to try breaking it.
Change recruitment and treatment assignment to separate association, causal effects among volunteers, and population generalization.
See an extreme observation pull the mean away from the median.
Change the bins without changing a single observation, and inspect the underlying counts.
Move one influential point and watch Pearson's r change.
Change survey participation while holding everyone's opinions fixed.
Generate sampling distributions from populations that are anything but bell-shaped.
Change an interval and watch its probability grow or shrink.
Change test accuracy and condition prevalence to see why false positives can dominate.
Build fifty intervals repeatedly; see how many contain a fixed true mean.
Change the head count and read an exact two-sided p-value.
Switch between aggregate and stratified published data to discover a reversal.
Resample waiting times with replacement and examine the estimated uncertainty of the mean.
Enumerate all seventy possible treatment allocations for a tiny randomized experiment.
Introduce curvature, refit a line, and watch residual structure emerge.
For instructors: predict → experiment → explain teaching prompts and printable student worksheets are available.
The thirteen guided laboratories run entirely in your browser, with no required account or server-side computation.