In hypothesis testing, a significance level of 0.05 indicates what?

A significance level of 0.05 indicates a 5% chance of making a Type I error, which is the probability of incorrectly rejecting the null hypothesis when it is actually true. In hypothesis testing, setting a significance level (often referred to as alpha) establishes a critical threshold for deciding whether the evidence observed in the data is strong enough to reject the null hypothesis. When the significance level is set at 0.05, it means that there is a tolerance for a 5% error rate in this decision-making process.

This choice aligns with the foundational principles of hypothesis testing, where researchers aim to control the likelihood of making errors in their conclusions. If a p-value resulting from the data analysis is less than or equal to 0.05, then the null hypothesis is typically rejected in favor of the alternative hypothesis, indicating that the results are statistically significant.

The other options do not correctly define the significance level. While a confidence level of 95% is related to the significance level, it does not directly describe what the alpha level signifies regarding error rates. The probability of rejecting the null hypothesis is the result of the hypothesis test, not an inherent property of the significance level itself. Additionally, the significance level is not used as a criterion for