1. What is Power Analysis?

Power analysis is a statistical method used to determine the sample size required to detect an effect of a given size with a certain degree of confidence. It helps researchers design studies that are adequately powered to test their hypotheses.

2. How Does the Calculator Work?

The calculator uses the power analysis formula for t-test:

Where:

• \( Z_{1-\alpha/2} \) — Z-score for significance level (alpha)
• \( Z_{1-\beta} \) — Z-score for power
• \( \sigma_1, \sigma_2 \) — Standard deviations of groups 1 and 2
• \( n_1, n_2 \) — Sample sizes of groups 1 and 2
• \( \delta \) — Effect size (minimum detectable difference)

Explanation: This formula calculates the required sample size to achieve specified statistical power for detecting a given effect size.

3. Importance of Sample Size Calculation

Details: Proper sample size calculation ensures studies have sufficient power to detect meaningful effects while avoiding wasted resources on underpowered studies or unnecessary large samples.

4. Using the Calculator

Tips: Enter alpha (typically 0.05), desired power (typically 0.8), standard deviations for both groups, current sample sizes, and the minimum effect size you want to detect.

5. Frequently Asked Questions (FAQ)

Q1: What is a good power level?
A: Typically 0.8 or 0.9 is recommended, meaning 80% or 90% chance of detecting a true effect.

Q2: How do I determine effect size?
A: Effect size can be based on previous studies, pilot data, or the minimum clinically important difference.

Q3: What if I have unequal group sizes?
A: The calculator accounts for different sample sizes in groups 1 and 2 through the n₁ and n₂ parameters.

Q4: When should I use this calculator?
A: Use for planning two-group comparison studies (t-tests) where you want to ensure adequate statistical power.

Q5: What are common alpha values?
A: Typically 0.05 for a 5% significance level, but can be adjusted based on study requirements.