09. Types of Errors - Part II

Type I Errors

Type I errors have the following features:

1. You should set up your null and alternative hypotheses, so that the worse of your errors is the type I error.
2. They are denoted by the symbol $\alpha$.
3. The definition of a type I error is: Deciding the alternative ($H_1$) is true, when actually ($H_0$) is true.
4. Type I errors are often called false positives.

Type II Errors

1. They are denoted by the symbol $\beta$.
2. The definition of a type II error is: Deciding the null ($H_0$) is true, when actually ($H_1$) is true.
3. Type II errors are often called false negatives.

In the most extreme case, we can always choose one hypothesis (say always choosing the null) to ensure that a particular error never occurs (never a type I error assuming we always choose the null). However, more generally, there is a relationship where with a single set of data decreasing your chance of one type of error, increases the chance of the other error occurring.