The Power of the Test is the probability that a statistical test will correctly reject a false null hypothesis. It measures the sensitivity of the test in detecting an effect when there is one.
5 Must Know Facts For Your Next Test
Power is calculated as $1 - \beta$, where $\beta$ is the probability of making a Type II error.
Higher power reduces the risk of a Type II error, meaning it increases the likelihood of detecting a true effect.
Factors that can increase power include larger sample sizes, higher significance levels ($\alpha$), and larger effect sizes.
Power analysis can be used to determine the required sample size for a study before data collection begins.
A commonly accepted minimum value for power in many fields is 0.80, indicating an 80% chance of correctly rejecting a false null hypothesis.
Review Questions
What role does sample size play in influencing the power of a test?
How is power related to Type I and Type II errors?
Why might researchers conduct a power analysis before starting an experiment?
Related terms
Type I Error: The incorrect rejection of a true null hypothesis, also known as a 'false positive.'
Type II Error: The failure to reject a false null hypothesis, also known as a 'false negative.'
$\alpha$ Level: The threshold probability set by researchers below which they will reject the null hypothesis, typically set at 0.05.