1. What is Statistical Power?

Statistical power is the probability that a test will correctly reject a false null hypothesis (avoid a Type II error). Power analysis helps determine the sample size needed to detect an effect of a given size with a certain degree of confidence.

2. How JMP Calculates Power

JMP uses sophisticated algorithms to calculate power based on:

Where:

• \( \alpha \) — Significance level (Type I error rate)
• \( \beta \) — Type II error rate
• Effect size — Standardized magnitude of the effect
• Sample size — Number of observations per group

Note: This calculator provides an approximation. For exact calculations, use JMP's built-in power analysis tools.

3. Importance of Power Analysis

Details: Conducting power analysis before a study helps ensure you have adequate sample size to detect meaningful effects, preventing wasted resources on underpowered studies.

4. Using the Power Calculator

Tips: Enter the significance level (typically 0.05), expected effect size (Cohen's d), sample size per group, and select one- or two-tailed test. The calculator will estimate the statistical power.

5. Frequently Asked Questions (FAQ)

Q1: What is a good power level?
A: Typically 0.8 (80%) or higher is considered adequate, though this depends on the study context.

Q2: How does effect size affect power?
A: Larger effect sizes are easier to detect, requiring smaller sample sizes for the same power.

Q3: What if my power is too low?
A: Consider increasing sample size, using more precise measurements, or accepting a larger effect size.

Q4: How does alpha affect power?
A: Lower alpha (e.g., 0.01 instead of 0.05) reduces power, making it harder to detect effects.

Q5: Can I calculate required sample size?
A: Yes, in JMP you can solve for sample size given desired power, effect size, and alpha.