Reducing the probability of a Type I error also reduces the probability of a Type II error.

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Reducing the probability of a Type I error (rejecting the null hypothesis when it is true) does not necessarily reduce the probability of a Type II error (failing to reject the null hypothesis when it is false). In fact, decreasing the probability of a Type I error often increases the probability of a Type II error and vice versa, creating a tradeoff between the two types of errors. This is known as the "power" of a statistical test, which is the ability to correctly detect a true effect. Increasing sample size and/or using a more stringent significance level can decrease Type II error rates, but at the expense of increasing Type I error rates. Finding an optimal balance between Type I and II error rates requires careful consideration of the research question, practical constraints, and statistical assumptions.

Reducing the Probability of a Type I Error Also Reduces