1. What is Statistical Power?

Statistical power (P) is the probability that a statistical test will correctly reject a false null hypothesis. It represents the test's ability to detect an effect when one truly exists, calculated as 1 minus the Type II error rate (β).

2. How Does the Calculator Work?

The calculator uses the statistical power formula:

Where:

• \( P \) — Statistical power (0 to 1)
• \( \beta \) — Type II error rate (0 to 1)

Explanation: This simple formula calculates the probability of correctly rejecting a false null hypothesis in statistical testing.

3. Importance of Statistical Power

Details: Adequate statistical power is crucial for reliable research results. Low power increases the risk of false negatives (Type II errors), while high power ensures better detection of true effects.

4. Using the Calculator

Tips: Enter the Type II error rate (β) as a value between 0 and 1. The calculator will compute the corresponding statistical power (P = 1 - β).

5. Frequently Asked Questions (FAQ)

Q1: What is considered good statistical power?
A: Typically, a power of 0.8 (80%) or higher is considered adequate in most research studies.

Q2: How does sample size affect statistical power?
A: Larger sample sizes generally increase statistical power, making it easier to detect true effects.

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

Q4: What's the relationship between α and β errors?
A: Type I error (α) is rejecting a true null hypothesis, while Type II error (β) is failing to reject a false null hypothesis.

Q5: When should power analysis be conducted?
A: Power analysis should be performed before data collection to determine adequate sample size for detecting expected effects.