In hypothesis testing, what does Beta represent?

In hypothesis testing, what does Beta represent?

• A. The probability of a Type II error.
• B. The probability of a Type I error.
• C. The effect size.
• D. The power of the test.

Answer: (A)

Beta represents the probability of a Type II error in hypothesis testing: the chance of not detecting a real effect when one actually exists. In other words, it is the likelihood of failing to reject the null when the null is false. This is why beta is closely linked to the test’s power, which is the probability of correctly rejecting a false null—power equals 1 minus beta. It’s distinct from the probability of a Type I error (alpha), which is about wrongly rejecting a true null, and from the effect size, which describes how large the real difference is rather than the likelihood of making an error. In practice, increasing sample size or adjusting the significance criteria can reduce beta and boost power, making it more likely to detect true effects.

Beta represents the probability of a Type II error in hypothesis testing: the chance of not detecting a real effect when one actually exists. In other words, it is the likelihood of failing to reject the null when the null is false. This is why beta is closely linked to the test’s power, which is the probability of correctly rejecting a false null—power equals 1 minus beta. It’s distinct from the probability of a Type I error (alpha), which is about wrongly rejecting a true null, and from the effect size, which describes how large the real difference is rather than the likelihood of making an error. In practice, increasing sample size or adjusting the significance criteria can reduce beta and boost power, making it more likely to detect true effects.