Statistical power is typically calculated using the following parameters:
Sample Size: Larger sample sizes generally increase statistical power. Effect Size: Larger effect sizes are easier to detect and thus increase power. Significance Level (α): Lower significance levels (e.g., 0.01) decrease power, while higher levels (e.g., 0.05) increase it. Variability: Lower variability in data increases power.
Researchers often use software tools and power analysis calculators to determine the required sample size to achieve a desired level of power, usually set at 0.80 or 80%.
