Dev/DataAnalysis
Data Analysis - Types of Errors
healthyryu
2019. 6. 12. 08:49
Type I Errors
1. You should set up your null and alternative hypotheses, so that the worst of your errors is the type I error.
2. They are denoted by the symbol α.
3. The definition of a type I error is: Deciding the alternative (H1) is true, when actually (H0) is true.
4. Type I errors are often called false positives.
Type II Errors
1. They are denoted by the symbol β.
2. The definition of a type II error is: Deciding the null (H0) is true, when actually (H1) is true.
3. Type II errors are often called false negatives.
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