What is a common statistical significance threshold used in hypothesis testing?

In hypothesis testing, a p-value is used to determine the significance of the results obtained from a statistical test. The common threshold for statistical significance is a p-value of less than 0.05. This threshold indicates that there is less than a 5% probability that the observed data would occur under the null hypothesis, suggesting that the null hypothesis can be rejected in favor of the alternative hypothesis.

Using this threshold allows researchers to balance the risk of Type I errors (rejecting a true null hypothesis) with the need for practicality in determining whether findings are statistically significant. A p-value below this threshold provides evidence strong enough to support a conclusion that the effect being tested is likely not due to chance.

Although thresholds such as p-values less than 0.01 are also used in certain contexts, they represent a more stringent criterion for significance and are not the most commonly used threshold in general practice. The option indicating a p-value exactly at 0.05 implies that the outcome is marginal, and a p-value greater than 0.05 suggests that the null hypothesis should not be rejected, which does not align with common practice for establishing significance.