The evidence is collected in the form of a sample, and the statistician must then decide. There are two possible errors. The statistician could mistakenly reject a true null hypothesis (called a Type I error), or mistakenly accept a false null hypothesis (called a Type II error).The benefit of the doubt goes to the null hypothesis, which is assumed to be true until the evidence seems to indicate otherwise.he level of significance $\alpha$ is the probability of a Type I error. That is, $\alpha$ is the probability of rejecting a true null hypothesis. In our example above, that probability was 5%.
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