# Learn R: Hypothesis Testing

Learn about the statistics used to run hypothesis tests. Then, learn how to use R to run different t-tests that compare distributions.

Start## Key Concepts

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Hypothesis Test Errors

Hypothesis Test Errors

*Type I* errors, also known as *false positives*, is the error of rejecting a null hypothesis when it is actually true. This can be viewed as a miss being registered as a hit. The acceptable rate of this type of error is called *significance level* and is usually set to be `0.05`

(5%) or `0.01`

(1%).

*Type II* errors, also known as *false negatives*, is the error of not rejecting a null hypothesis when the alternative hypothesis is the true. This can be viewed as a hit being registered as a miss.

Depending on the purpose of testing, testers decide which type of error to be concerned. But, usually `type I`

error is more important than `type II`

.

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