1. What is the Sample Size by Prevalence?

The sample size calculation by prevalence determines the number of participants needed in a study to estimate a population proportion with a specified level of confidence and precision. It's essential for designing prevalence studies and surveys.

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)
• \( p \) — Expected prevalence (proportion between 0 and 1)
• \( d \) — Desired precision (margin of error)

Explanation: The formula accounts for the relationship between confidence level, expected prevalence, and desired precision in estimating a population proportion.

3. Importance of Sample Size Calculation

Details: Proper sample size calculation ensures studies have adequate power to detect effects while avoiding unnecessary resource expenditure on overly large samples.

4. Using the Calculator

Tips: Enter Z-score (typically 1.96 for 95% confidence), expected prevalence (0-1), and desired precision (margin of error). All values must be valid (Z > 0, 0 ≤ p ≤ 1, d > 0).

5. Frequently Asked Questions (FAQ)

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

Q2: What if I don't know the expected prevalence?
A: Use p=0.5 as this gives the most conservative (largest) sample size estimate.

Q3: How do I choose the precision (d)?
A: Smaller d gives more precise estimates but requires larger samples. Common values are 0.05 or 0.03.

Q4: Does this work for small populations?
A: For populations under 10,000, consider using the finite population correction.

Q5: What about non-response or attrition?
A: Increase your calculated sample size by your expected non-response rate.