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This is a great explanation of Type I error vs a Type II error. Why do you think it is important to avoid making one of these errors? How do you think it could affect a population if one of these errors did occur and went undetected?


Type I error:

A Type I error occurs when the researcher rejects a null hypothesis when it is true. The probability of committing a Type I error is called the significance level. This probability is also called alpha .

Type II error:

A type II error is also known as a false negative and occurs when a researcher fails to reject a null hypothesis which is actually false.

To avoid type II error:

The probability of a type 1 error (rejecting a true null hypothesis) can be minimized by picking a smaller level of significance α before doing a test.

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