1. What is Sample Size Calculation?

Sample size calculation determines the number of participants needed in a study to detect a statistically significant effect. It ensures research has adequate power while optimizing resources and minimizing costs.

2. How Does the Calculator Work?

The calculator uses the standard sample size formula:

Where:

• \( n \) — Required sample size
• \( Z \) — Z-score corresponding to confidence level
• \( p \) — Estimated proportion or probability
• \( E \) — Margin of error

Explanation: The formula calculates the minimum number of samples needed to achieve desired precision in estimating population parameters.

3. Importance of Sample Size Calculation

Details: Proper sample size ensures study validity, prevents underpowered studies, optimizes resource allocation, and meets statistical requirements for publication.

4. Using the Calculator

Tips: Enter Z-score based on confidence level (1.96 for 95%, 2.58 for 99%), estimated proportion (use 0.5 for maximum variability), and desired margin of error (typically 0.05 for 5%).

5. Frequently Asked Questions (FAQ)

Q1: What Z-score should I use?
A: Common Z-scores are 1.645 (90% confidence), 1.96 (95% confidence), and 2.576 (99% confidence).

Q2: What if I don't know the proportion?
A: Use 0.5 (50%) as it provides the most conservative (largest) sample size estimate.

Q3: What is a reasonable margin of error?
A: Typically 0.05 (5%) for most studies, but can range from 0.01 to 0.10 depending on precision needs.

Q4: Does this work for all study types?
A: This formula is for proportion studies. Different formulas exist for means, correlations, and other statistical tests.

Q5: What about population size?
A: This formula assumes large populations. For small populations, use finite population correction.