1. What is the Sample Size From Prevalence Formula?

The sample size from prevalence formula calculates the required sample size for a study based on expected prevalence, desired confidence level (Z-score), and acceptable margin of error. This is essential for designing epidemiological studies and surveys.

2. How Does the Calculator Work?

The calculator uses the sample size formula:

Where:

• \( n \) — Required sample size
• \( Z \) — Z-score corresponding to desired confidence level
• \( p \) — Expected prevalence (0 to 1)
• \( e \) — Margin of error (0 to 1)

Explanation: The formula calculates the minimum sample size needed to estimate prevalence within a specified margin of error at a given confidence level.

3. Importance of Sample Size Calculation

Details: Proper sample size calculation ensures study results are statistically significant, prevents underpowered studies, and optimizes resource allocation in research projects.

4. Using the Calculator

Tips: Enter Z-score (e.g., 1.96 for 95% confidence), expected prevalence (as decimal between 0-1), and desired margin of error (as decimal between 0-1). All values must be valid.

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 prevalence is unknown?
A: Use p = 0.5 for maximum variability, which gives the most conservative (largest) sample size estimate.

Q3: How does margin of error affect sample size?
A: Smaller margin of error requires larger sample size. Halving the error quadruples the required sample size.

Q4: When is this formula appropriate?
A: For simple random sampling when estimating a proportion (prevalence) in a large population.

Q5: What about finite population correction?
A: For small populations, apply finite population correction: n_adjusted = n / (1 + (n-1)/N) where N is population size.