1. What is Sample Size Calculation?

Sample size calculation determines the minimum number of participants needed in a study to achieve statistically significant results. It ensures that research findings are reliable and representative of the population.

2. How Does the Calculator Work?

The calculator uses the standard sample size formula for proportions:

Where:

• \( n \) — Minimum sample size required
• \( Z \) — Z-score corresponding to the desired confidence level
• \( p \) — Estimated proportion or probability of success
• \( E \) — Margin of error (precision)

Explanation: This formula calculates the minimum number of observations needed to estimate a population proportion with specified confidence and precision.

3. Importance of Sample Size Determination

Details: Proper sample size calculation is crucial for study validity, ensuring adequate statistical power, minimizing type I and II errors, and making research findings generalizable to the target population.

4. Using the Calculator

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

5. Frequently Asked Questions (FAQ)

Q1: What Z-score should I use?
A: Common Z-scores are 1.645 (90% CI), 1.96 (95% CI), and 2.576 (99% CI). Choose based on your desired confidence level.

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: How do I choose the margin of error?
A: Smaller margins (e.g., 0.01) provide more precision but require larger samples. Typical values range from 0.01 to 0.1.

Q4: When is this formula appropriate?
A: For studies estimating population proportions with simple random sampling. Different formulas exist for means, correlations, and complex sampling designs.

Q5: What about population size?
A: This formula assumes infinite population. For finite populations, apply the finite population correction factor.