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 minimizing resource waste and ethical concerns.

2. How Does the Calculator Work?

The calculator uses the sample size formula for proportions:

Where:

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

Explanation: This formula calculates the minimum number of participants needed to estimate a population proportion within a specified margin of error at a given confidence level.

3. Importance of Sample Size in Research

Details: Proper sample size ensures study validity, statistical power, and reliable results. Too small samples may miss true effects, while too large samples waste resources.

4. Using the Calculator

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

5. Frequently Asked Questions (FAQ)

Q1: What Z-score should I use?
A: Use 1.96 for 95% confidence level, 2.58 for 99% confidence level, or 1.645 for 90% confidence level.

Q2: Why use 0.5 for proportion?
A: Using p=0.5 gives the most conservative (largest) sample size, ensuring adequate power regardless of the true proportion.

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

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

Q5: Should I adjust for population size?
A: For populations larger than 20,000, no adjustment is needed. For smaller populations, use finite population correction.