The Type II error rate, often denoted as \(\beta\), refers to the probability of failing to reject a null hypothesis when it is actually false. This concept is crucial in statistical hypothesis testing, particularly in contexts where repeated measures are involved, as it can affect the interpretation of results and the power of the tests used. Understanding Type II error rate helps researchers assess the risks of overlooking a true effect or difference, especially when analyzing data that involves multiple measurements from the same subjects.
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