Q1. What does a p-value represent?
The probability of observing data at least as extreme as what was seen, assuming the null hypothesis is true The probability that the null hypothesis is true The probability that the alternative hypothesis is true The effect size of the observed result
Q2. What is a Type I error?
Rejecting a true null hypothesis (a false positive) Failing to reject a false null hypothesis (a false negative) Using the wrong statistical test Having too small a sample size
Q3. What does the significance level (α), commonly set to 0.05, represent?
The threshold p-value below which you reject the null hypothesis; also the accepted Type I error rate The probability the alternative hypothesis is true The statistical power of the test The confidence that the effect size is large
Q4. What does statistical power measure?
The probability of correctly rejecting a false null hypothesis (avoiding a Type II error) The probability of a Type I error The sample's standard deviation The strength of correlation between variables
