### Type I and Type II errors

A Type I error is the rejection of a true null hypothesis, therefore finding an incorrect significance with a false positive.

A Type II error is the failure to reject a false null hypothesis, therefore failing to define a true significance with a false negative.

You select a small p-value to minimise the probability of a Type I error and choose a critical region that minimises the probability of a Type II error.

When you set a rigorous threshold for your p-value, such as 0.01 you stand more risk of a Type II error, or with a threshold that’s too relaxed with a low significance level you risk a Type I error. This is why choosing an appropriate p-value is so critical. If for example, you set a relaxed 0.1 threshold in court then 1 in every 10 defendants found guilty would actually be innocent.