Key Concepts
- There are two types of errors that can happen in hypothesis testing: Type I and Type II.
- A Type I error is rejecting the null hypothesis when the null hypothesis is true.
- The probability of a Type I error is called alpha, [latex]\alpha[/latex].
- A Type II error is not rejecting the null hypothesis when the null hypothesis is false.
- The probability of a Type II error is called beta, [latex]\beta[/latex].
Power is the ability of a test to correctly reject a false null hypothesis. Power equals [latex]1 - \beta[/latex].
Glossary
Level of Significance of the Test: Probability of a Type I error (reject the null hypothesis when it is true). Notation: [latex]\alpha[/latex]. In hypothesis testing, the Level of Significance is called the preconceived [latex]\alpha[/latex] or the preset [latex]\alpha[/latex].
Type 1 Error: The decision is to reject the null hypothesis when, in fact, the null hypothesis is true.
Type 2 Error: The decision is not to reject the null hypothesis when, in fact, the null hypothesis is false.
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- Introductory Statistics. Authored by: Barbara Illowsky, Susan Dean. Provided by: OpenStax. Located at: https://openstax.org/books/introductory-statistics/pages/9-key-terms. License: CC BY: Attribution. License Terms: Access for free at https://openstax.org/books/introductory-statistics/pages/1-introduction