1. What is T Test Power?

Statistical power in a t-test represents the probability that the test will correctly reject a false null hypothesis. It is calculated as 1 - β, where β is the probability of Type II error.

2. How Does the Calculator Work?

The calculator uses the power formula:

Where:

• \( \beta \) — Type II error probability (false negative rate)
• \( Power \) — Statistical power (true positive rate)

Explanation: This simple calculation shows the complement relationship between power and Type II error probability.

3. Importance of Power Calculation

Details: Adequate statistical power is crucial for study design and interpretation. High power reduces the risk of false negative results and increases confidence in study findings.

4. Using the Calculator

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

5. Frequently Asked Questions (FAQ)

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

Q2: How does sample size affect power?
A: Larger sample sizes generally increase statistical power, while smaller samples decrease power.

Q3: What factors influence t-test power?
A: Effect size, sample size, alpha level, and variability in the data all affect statistical power.

Q4: Can power be too high?
A: While higher power is generally better, extremely high power might indicate unnecessarily large sample sizes that waste resources.

Q5: How is power used in study planning?
A: Power analysis helps determine the minimum sample size needed to detect an effect of a given size with a specified level of confidence.