A Prior belief refers to how we believe experiments are distributed for all tests. We use it in the stats model to temper the effect of random fluctuations, especially early on in tests. Without it, the first few thousand visitors would have wildly varying confidence intervals. It also evokes a user to compare expected and raw uplift. Meaning that while we believe there’s an uplift, we think it’s likely that some of the uplift came from random fluctuation.