1. What is the Sample Size Calculation?

The sample size calculation determines the number of participants needed in a prevalence study to achieve statistically significant results, given the expected standard deviation and desired margin of error.

2. How Does the Calculator Work?

The calculator uses the sample size formula:

Where:

• \( n \) — Required sample size
• \( Z \) — Z-score for desired confidence level (1.96 for 95% CI)
• \( \sigma \) — Standard deviation of the measure
• \( d \) — Margin of error (precision)

Explanation: The formula accounts for the variability in data (σ) and the desired precision (d) to determine how many participants are needed.

3. Importance of Sample Size Calculation

Details: Proper sample size ensures study results are statistically valid and reliable. Too small a sample may miss true effects, while too large wastes resources.

4. Using the Calculator

Tips: Enter Z-score (typically 1.96 for 95% CI), estimated standard deviation from pilot data, and desired margin of error. All values must be positive numbers.

5. Frequently Asked Questions (FAQ)

Q1: What Z-score should I use?
A: 1.96 for 95% confidence, 2.58 for 99% confidence, or other values from Z-tables for different confidence levels.

Q2: How to estimate standard deviation?
A: Use data from pilot studies, similar research, or literature. For proportions, σ = √(p(1-p)) where p is expected prevalence.

Q3: What's a reasonable margin of error?
A: Typically 5% or less, but depends on research goals. Smaller d requires larger n.

Q4: Does this work for all study types?
A: This formula is for continuous outcomes. Different formulas exist for proportions, odds ratios, etc.

Q5: Should I adjust for expected dropout?
A: Yes, increase sample size by expected dropout rate (e.g., if n=100 and 20% dropout expected, recruit 125).