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). It's a crucial concept in hypothesis testing and study design.

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 hypothesis when it's false

Explanation: Power represents the test's ability to detect an effect when one truly exists. Higher power means a lower chance of missing real effects.

3. Importance of Power Calculation

Details: Power analysis is essential for study design. Researchers typically aim for 80% power (β = 0.20) or higher to ensure adequate sensitivity.

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's a good power value for a study?
A: Most studies aim for 80% power (β = 0.20) or 90% power (β = 0.10), balancing sensitivity with practical constraints.

Q2: How is power related to sample size?
A: Power increases with larger sample sizes, effect sizes, and higher significance levels (α).

Q3: What factors affect statistical power?
A: Sample size, effect size, significance level (α), and variability in the data all influence power.

Q4: Can power be too high?
A: While higher power is generally better, extremely high power may require impractical sample sizes or detect trivial effects.

Q5: How is this different from Type I error?
A: Type I error (α) is a false positive, while Type II error (β) is a false negative. Power is the complement of Type II error.