What term refers to the chance of obtaining a significant result under the null hypothesis assumption?

The significance level, often denoted as alpha, represents the probability of incorrectly rejecting the null hypothesis when it is actually true. It essentially sets the threshold for determining whether a result is statistically significant. When researchers conduct a hypothesis test, they typically choose a significance level (commonly 0.05) to decide if the observed data falls into the critical region that would lead to rejecting the null hypothesis.

The significance level directly correlates to the risk of a Type I error, which occurs when the null hypothesis is false but is rejected based on the sample results. Therefore, when considering the scenario of obtaining a significant result under the null hypothesis, the significance level reflects the predetermined chance of making this error, thereby highlighting its role in hypothesis testing and interpreting statistical results.

To amplify understanding, the other terms mentioned serve different purposes in statistical analysis. The power of the test quantifies the probability of correctly rejecting the null hypothesis when it is false. A Type II error refers to the failure to reject the null hypothesis when it is indeed false. The P-value assesses the strength of the evidence against the null hypothesis but does not represent the predetermined chance of obtaining a significant result under it. Thus, the significance level is the correct term representing the chance of obtaining