The probability of a Type II error decreases as the "true" population value gets farther from the hypothesized population value, given that everything else is held constant.

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As the true population value moves further away from the hypothesized value, the difference between the two becomes more pronounced, making it easier to detect a true effect and thus reducing the probability of a Type II error, which is failing to reject a false null hypothesis.

The Probability of a Type II Error Decreases as the "True