What is the usual level of significance in hypothesis testing?

What is the usual level of significance in hypothesis testing?

• A. The level of significance is usually set at 0.01.
• B. The level of significance is usually set at a=0.05.
• C. The level of significance is never predetermined.
• D. The p-value must always be greater than 0.05.

Answer: (B)

In hypothesis testing, the level of significance (alpha) is the threshold set before you collect data to decide whether to reject the null hypothesis. The usual level is 0.05, meaning there’s a 5% risk of incorrectly rejecting a true null hypothesis (a Type I error). This default provides a practical balance between being too strict and too lenient, and it’s widely used across many fields unless a study requires a more stringent or more lenient standard. Before analyzing, you fix alpha; after computing the p-value from your data, you compare it to alpha. If the p-value is less than or equal to alpha, you declare the result statistically significant and reject the null. If it’s greater, you do not reject the null. The other statements don’t fit: 0.01 is used in some contexts but is not the usual default; alpha is predetermined before the study, not chosen after seeing the data; and the p-value does not have to be greater than 0.05—significance depends on comparing the p-value to alpha, with rejection occurring when the p-value is small enough.

In hypothesis testing, the level of significance (alpha) is the threshold set before you collect data to decide whether to reject the null hypothesis. The usual level is 0.05, meaning there’s a 5% risk of incorrectly rejecting a true null hypothesis (a Type I error). This default provides a practical balance between being too strict and too lenient, and it’s widely used across many fields unless a study requires a more stringent or more lenient standard.

Before analyzing, you fix alpha; after computing the p-value from your data, you compare it to alpha. If the p-value is less than or equal to alpha, you declare the result statistically significant and reject the null. If it’s greater, you do not reject the null.

The other statements don’t fit: 0.01 is used in some contexts but is not the usual default; alpha is predetermined before the study, not chosen after seeing the data; and the p-value does not have to be greater than 0.05—significance depends on comparing the p-value to alpha, with rejection occurring when the p-value is small enough.