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 ranges from 0 to 1, with higher values indicating greater sensitivity to detect effects.

2. How Does the Calculator Work?

The calculator uses the fundamental power formula:

Where:

• \( \beta \) — Type II error probability (false negative rate)
• Power — Probability of correctly rejecting the null when it's false

Explanation: This simple relationship shows that as Type II error decreases, power increases proportionally.

3. Importance of Power Analysis

Details: Power analysis is essential for study design to ensure adequate sample size. Most studies aim for power ≥0.80 (β ≤0.20).

4. Using the Calculator

Tips: Enter the Type II error probability (β) as a decimal between 0 and 1. The calculator will compute Power = 1 - β.

5. Frequently Asked Questions (FAQ)

Q1: What is an acceptable power level?
A: Typically 0.80 or higher, though some studies require 0.90 or more depending on consequences of Type II errors.

Q2: How is β related to sample size?
A: Larger sample sizes decrease β (increase power) for a given effect size. Power analysis helps determine required sample size.

Q3: What affects statistical power?
A: Effect size, sample size, α level (Type I error rate), and variability in the data all influence power.

Q4: Can power be too high?
A: Excessive power may detect trivial effects. Balance is needed between sensitivity and practical significance.

Q5: How is this different from post-hoc power?
A: This calculator shows the basic relationship. Actual study power depends on specific test parameters (effect size, sample size, etc.).