1. What is Sample Size Calculation Using Prevalence?

This calculator determines the minimum number of participants needed in a study to estimate a population proportion (prevalence) with a specified level of confidence and precision.

2. How Does the Calculator Work?

The calculator uses the sample size formula for prevalence studies:

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 variability in the population (p(1-p)) and the desired level of precision (d) to determine how many observations are needed.

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 (use 0.5 for maximum variability if uncertain), and desired precision (e.g., 0.05 for ±5% margin of error).

5. Frequently Asked Questions (FAQ)

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

Q2: What if I don't know the expected prevalence?
A: Using p=0.5 gives the most conservative (largest) sample size as it maximizes p(1-p).

Q3: How does precision affect sample size?
A: Smaller precision values (tighter margins of error) require dramatically larger samples (inverse square relationship).

Q4: What about finite population correction?
A: For small populations (n > 5% of population), apply the correction: n_adjusted = n/(1 + (n-1)/N).

Q5: Are there other factors to consider?
A: Account for expected non-response or dropout by increasing the calculated sample size accordingly.