Inference Lab
Run simulations under a null hypothesis and watch the p-value take shape. Enter your scenario, choose how many trials to run, and see exactly which simulated outcomes count as evidence against H₀.
What is this tool showing?
A simulation generates many hypothetical samples under the assumption that the null hypothesis is true. Each simulated sample produces a sample proportion, and those proportions form the null distribution shown in the histogram.
The p-value is the proportion of simulated results that are at least as extreme as the result you actually observed. The highlighted bars in the histogram are the ones that count.
A small p-value means that results as extreme as yours are rare under the null hypothesis — which is evidence against it. A large p-value means your result is consistent with what the null hypothesis would predict.
The p-value is not the probability that the null hypothesis is true. It is the probability of obtaining a result at least as extreme as the observed result, assuming the null hypothesis were true.