The opposite of the result you’re trying to prove.

In A/B testing, the goal is to show one variant outperforms the other. But, a null hypothesis assumes there’s no difference between variants A and B; both perform equally effectively.

When you run an A/B test and discover, for example, variant B converts 5% better than variant A, your null hypothesis is proven incorrect.

You find there is indeed a difference between versions A and B.