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.
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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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