University of Central Florida (UCF) QMB3200 Quantitative Business Tools II Midterm Practice Exam

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What does "statistical power" in hypothesis testing refer to?

The ability to correctly reject the null hypothesis when it’s true

The probability of correctly rejecting the null hypothesis when it is false

Statistical power in hypothesis testing refers to the probability of correctly rejecting the null hypothesis when it is false. This concept is crucial because it reflects the test’s ability to detect an effect or difference when one truly exists. A higher statistical power means that there is a greater likelihood of identifying a true positive outcome, which is essential for effective decision-making in a research context.

In practice, statistical power is influenced by several factors, including the sample size, the effect size, and the significance level set for the test. When researchers conduct studies, they strive for sufficient power to ensure that meaningful effects are not overlooked due to insufficient sample size or other limitations. Therefore, understanding and calculating statistical power helps in designing robust studies capable of yielding reliable conclusions.

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The likelihood of finding any statistical significance

The confidence in the sample's representation

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