American Board of Surgery Qualifying Exam (ABS QE) Practice Test

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What does a Type I error indicate in hypothesis testing?

Accepting a false null hypothesis
Rejecting a true null hypothesis
Finding no difference when one exists
Confirming a true null hypothesis

The correct answer is: Rejecting a true null hypothesis

A Type I error indicates the incorrect rejection of a true null hypothesis. This situation arises when researchers conclude that there is a statistically significant effect or difference when, in reality, none exists. The probability of making a Type I error is denoted by the significance level (alpha), often set at 0.05, which means there is a 5% risk of rejecting the null hypothesis when it is true. In hypothesis testing, the null hypothesis typically represents the status quo or no effect, while the alternative hypothesis suggests that there is an effect or difference. When the null hypothesis is true and the results suggest otherwise, it leads to the erroneous conclusion that an effect exists, which characterizes a Type I error. Understanding this concept is crucial in the realm of statistics and research, as it helps in evaluating the reliability of test results and the potential consequences of making incorrect conclusions regarding hypotheses.