1. What is Sample Size Calculation?

Sample size calculation determines the number of participants needed in a study to detect a statistically significant effect. It's a crucial step in research design to ensure studies are adequately powered.

2. How Does the Calculator Work?

The calculator uses the sample size formula for means:

Where:

• \( n \) — Required sample size
• \( z \) — Z-score for desired confidence level
• \( \sigma \) — Standard deviation of the population
• \( e \) — Margin of error (precision)

Explanation: The formula calculates how many samples are needed to estimate a population mean within a specified margin of error at a given confidence level.

3. Importance of Sample Size Calculation

Details: Proper sample size ensures studies have sufficient power to detect effects while avoiding unnecessary resource expenditure. Underpowered studies may miss important findings, while oversized studies waste resources.

4. Using the Calculator

Tips:

• For 95% confidence, use z = 1.96
• Standard deviation can be estimated from pilot studies or literature
• Margin of error should reflect your desired precision
• All values must be positive numbers

5. Frequently Asked Questions (FAQ)

Q1: What z-score should I use?
A: Common values are 1.96 (95% CI), 1.645 (90% CI), or 2.576 (99% CI). Choose based on your desired confidence level.

Q2: How do I estimate standard deviation?
A: Use data from pilot studies, similar research, or published literature. When unsure, conservative (larger) estimates are preferable.

Q3: What's a reasonable margin of error?
A: This depends on your research context. Smaller margins increase precision but require larger samples. Typical values range from 1-10% of the expected mean.

Q4: Does this work for proportions?
A: No, this formula is for continuous data. For proportions, use \( n = (z^2 \times p(1-p)) / e^2 \) where p is the expected proportion.

Q5: Should I adjust for expected dropout?
A: Yes, if you anticipate participant attrition, increase your sample size accordingly (e.g., by 10-20% depending on expected dropout rate).