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, helping researchers determine appropriate sample sizes.

2. How Does the Calculator Work?

The calculator uses the fundamental power formula:

Where:

• \( \beta \) — Probability of Type II error (failing to reject a false null hypothesis)
• Power — Probability of correctly rejecting a false null hypothesis

Explanation: Power increases as β decreases. A study with 80% power has a 20% chance of Type II error.

3. Importance of Power Analysis

Details: Power analysis helps researchers design studies that are likely to detect effects if they exist. It's essential for determining sample size and interpreting negative results.

4. Using the Calculator

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

5. Frequently Asked Questions (FAQ)

Q1: What is considered good statistical power?
A: Typically 80% power is considered acceptable, though some fields prefer 90% or higher for important studies.

Q2: How is power related to sample size?
A: Power increases with larger sample sizes, all else being equal. Power analysis can determine the needed sample size.

Q3: What affects statistical power?
A: Power is affected by effect size, sample size, significance level (α), and variability in the data.

Q4: Should I calculate power before or after my study?
A: Ideally both - before to design the study (a priori power analysis) and after to interpret results (post hoc power analysis).

Q5: Can power be too high?
A: Excessive power (e.g., >99%) might indicate an unnecessarily large sample size, wasting resources to detect trivial effects.