Power Analysis using R

Thus, a rational approach to hypothesis testing will seek to reject a hypothesis if it is false and accept a hypothesis when it is true. The two types of error are rejecting the null hypothesis when it is true (Type I) and accepting the null hypothesis when it is false (Type II). In the Neyman-Pearson theory, it is usual to fix the Type I error probability (α) at some constant (often at 0.05, but not necessarily), and then choose a test which minimises the Type II error probability (β), conditional on α. The (null) hypothesis is then either rejected when the associated p-value for the test is less than α, or otherwise accepted.