1. What is Power in Statistics?

Statistical power is the probability that a test correctly rejects the null hypothesis when the alternative hypothesis is true. It's calculated as 1 - β, where β is the probability of Type II error (false negative).

2. How Does the Calculator Work?

The calculator uses the fundamental power formula:

Where:

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

Explanation: Higher power means greater chance of detecting an effect when one exists. Researchers typically aim for power ≥ 0.80.

3. Importance of Power Analysis

Details: Power analysis helps determine appropriate sample sizes for studies and assess the reliability of study results. Underpowered studies may fail to detect true effects.

4. Using the Calculator

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

5. Frequently Asked Questions (FAQ)

Q1: What is a good power value?
A: Typically 0.80 or higher is considered adequate, though some fields require 0.90 or higher.

Q2: How does sample size affect power?
A: Larger sample sizes generally increase power, all else being equal.

Q3: What factors influence statistical power?
A: Effect size, sample size, significance level (α), and variability in the data.

Q4: What's the relationship between α and β?
A: For fixed sample size, decreasing α (Type I error) typically increases β (Type II error), reducing power.

Q5: Can power be calculated before a study?
A: Yes, power analysis is often conducted during study design to determine required sample size.