The significance level, denoted by $\alpha$, is the probability of rejecting the null hypothesis when it is actually true. It represents the threshold for determining whether a result is statistically significant.
5 Must Know Facts For Your Next Test
Common significance levels are 0.05, 0.01, and 0.10.
A lower significance level means stricter criteria for rejecting the null hypothesis.
The significance level is chosen before conducting a hypothesis test.
If the p-value is less than or equal to $\alpha$, the null hypothesis is rejected.
Significance level controls the Type I error rate in hypothesis testing.
Review Questions
What does a significance level of 0.05 mean in terms of Type I error?
How do you determine whether to reject the null hypothesis using the p-value and significance level?
Why is it important to choose a significance level before conducting a hypothesis test?
Related terms
Null Hypothesis: A statement that there is no effect or no difference, which serves as a starting point for statistical testing.
Type I Error: The error that occurs when rejecting a true null hypothesis; also known as a false positive.
p-value: \text{The probability of obtaining test results at least as extreme as the observed results, assuming that the null hypothesis is true.}