What does a significance level of 0.05 indicate?

A significance level of 0.05 indicates that there is a 5% chance of rejecting a true null hypothesis, which is commonly referred to as a Type I error. In hypothesis testing, the significance level, or alpha level, is a threshold that determines when to reject the null hypothesis. If the p-value (the probability of obtaining the observed results under the null hypothesis) is less than the significance level, researchers conclude that the results are statistically significant and reject the null hypothesis.

Choosing a significance level of 0.05 means that if the null hypothesis is actually true, there is a 5% risk that the findings will lead to its rejection, thereby indicating a potential error in the decision-making process. This level is widely accepted in research as a reasonable balance between being too strict (leading to potential Type II errors) and too lenient (increasing the likelihood of Type I errors).

The other options relate to different statistical concepts: Type II error pertains to failing to reject a false null hypothesis, the likelihood of the null hypothesis being true does not align with a significance level, and the error margin in estimating population parameters relates to confidence intervals, not hypothesis testing. Hence, the defined significance level directly correlates to the probability associated with making