How to Calculate Sample Size Using Epi Info

Sample Size Formula for Proportions:

\[ n = \frac{Z^2 \times p \times (1-p)}{d^2} \]

Z-Score (Z):

(e.g., 1.96 for 95% CI)

Expected Proportion (p):

(0 to 1)

Precision (d):

(e.g., 0.05 for ±5%)

Required Sample Size (n):

Unit Converter ▲

Unit Converter ▼

From:	To:

1. What is Sample Size Calculation?

Sample size calculation is a crucial step in research design that determines the number of participants needed to detect a statistically significant effect. Epi Info implements the standard formula for proportion-based sample size calculations.

2. How Does the Formula Work?

The calculator uses the standard sample size formula for proportions:

\[ n = \frac{Z^2 \times p \times (1-p)}{d^2} \]

Where:

\( n \) — Required sample size
\( Z \) — Z-score corresponding to desired confidence level
\( p \) — Expected proportion in the population
\( d \) — Desired precision (margin of error)

Explanation: This formula calculates the minimum number of participants needed to estimate a population proportion with specified confidence and precision.

3. Importance of Sample Size Calculation

Details: Proper sample size calculation ensures studies have adequate power to detect effects, prevents wasted resources on underpowered studies, and provides valid statistical conclusions.

4. Using the Calculator

Tips: Enter Z-score (1.96 for 95% confidence), expected proportion (use 0.5 for maximum sample size), and desired precision. All values must be valid (Z > 0, p between 0-1, d > 0).

5. Frequently Asked Questions (FAQ)

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

Q2: What if I don't know the expected proportion?
A: Use 0.5 (50%) as this gives the maximum sample size and ensures adequate power regardless of the actual proportion.

Q3: How do I choose the precision (d)?
A: Precision represents your margin of error. Smaller values (e.g., 0.02) give more precise estimates but require larger samples.

Q4: Does this work for all study types?
A: This formula is specifically for estimating a single proportion. Different formulas are needed for means, comparing proportions, or regression analyses.

Q5: Should I adjust for expected response rate?
A: Yes, divide the calculated sample size by your expected response rate to account for non-response (e.g., if 80% response expected, divide by 0.8).