This statement is a representation of the relationship between the probabilities of type I and type II errors in statistical hypothesis testing. The sum of these two probabilities is always equal to 1. Type I Error (False Positive): The probability of rejecting a true null hypothesis. Type II Error (False Negative): The probability of failing to reject a false null hypothesis. Therefore, the sum of the probability of making a Type I error and the probability of making a Type II error equals 1. This relationship is fundamental in understanding the trade-off between these two types of errors in hypothesis testing.
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