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 is calculated as 1 - β, where β is the probability of a Type II error.

2. How Does Power Calculation Work?

The calculator estimates statistical power based on:

Where:

• \( \beta \) — Probability of Type II error (false negative)
• Sample Size — Number of observations in the study
• Effect Size — Standardized measure of the magnitude of the effect

Explanation: Power increases with larger sample sizes, larger effect sizes, and higher significance levels (α).

3. Importance of Power Analysis

Details: Power analysis helps researchers determine the minimum sample size needed to detect an effect of a given size with a certain degree of confidence, preventing underpowered studies.

4. Using the Calculator

Tips: Enter the sample size (number of observations), effect size (standardized measure), and β (probability of Type II error, typically 0.2 for 80% power).

5. Frequently Asked Questions (FAQ)

Q1: What is a good power level for a study?
A: Typically 80% or higher is considered acceptable, though some fields prefer 90% power.

Q2: How does effect size affect power?
A: Larger effect sizes require smaller sample sizes to achieve the same power. Small effect sizes require much larger samples.

Q3: What's the relationship between α and power?
A: Higher α (e.g., 0.05 vs 0.01) increases power but also increases Type I error risk.

Q4: Can you have too much power?
A: Excessive power may detect trivial effects as statistically significant, potentially leading to misinterpretation of clinical importance.

Q5: What if my study is underpowered?
A: An underpowered study may fail to detect true effects. Consider increasing sample size, using more precise measurements, or focusing on larger effects.