1. What is Minimum Sample Size Calculation?

Minimum sample size calculation determines the smallest number of participants needed in a study to achieve statistically significant results. It ensures that research has adequate power to detect effects while minimizing resource waste.

2. How Does the Calculator Work?

The calculator uses the standard sample size formula for proportion estimation:

Where:

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

Explanation: The formula balances confidence level (Z), expected variability (p), and desired precision (E) to determine the optimal sample size.

3. Importance of Sample Size Determination

Details: Proper sample size calculation is crucial for research validity. It prevents underpowered studies that may miss true effects and overpowered studies that waste resources on unnecessary participants.

4. Using the Calculator

Tips: Enter Z-score (1.96 for 95% confidence, 2.576 for 99%), estimated proportion (use 0.5 for maximum variability), and margin of error (typically 0.05 for 5% error).

5. Frequently Asked Questions (FAQ)

Q1: What Z-score should I use?
A: Use 1.96 for 95% confidence level, 1.645 for 90%, and 2.576 for 99% confidence level.

Q2: Why use p=0.5 when I don't know the proportion?
A: p=0.5 gives the maximum sample size, ensuring adequate power regardless of the actual proportion.

Q3: What is a reasonable margin of error?
A: Typically 0.05 (5%) for general research, but can be adjusted based on study requirements and precision needs.

Q4: Does this work for small populations?
A: For finite populations, apply the finite population correction: \( n_{adjusted} = \frac{n}{1 + \frac{(n-1)}{N}} \) where N is population size.

Q5: When should I use different sample size formulas?
A: Use different formulas for means comparison, correlation studies, or clinical trials with specific power requirements.