The Type II error rate, often denoted as \(\beta\), is the probability of failing to reject a null hypothesis when it is false. This concept is crucial for understanding the effectiveness of statistical tests, as it reflects the likelihood of missing a true effect or difference in a population. A high Type II error rate indicates a test that may not be sensitive enough to detect real changes, which can lead to incorrect conclusions in research.
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