Calculating the right sample size is one of the most crucial steps in any research study. Too small a sample can lead to under-powered studies, while overly large samples waste resources. This blog walks you through key concepts of sample size calculation, including when to use one-tail vs. two-tail tests, one-variable vs. two-variable designs, prevalence vs. effect size, and the specific requirements for pilot or feasibility studies.
1. Why Is Sample Size Important?
Sample size ensures your study has the statistical power to detect true effects and avoid false conclusions. An optimal sample size:
- Provides precise estimates.
- Reduces type I (false positives) and type II (false negatives) errors.
- Saves time, effort, and resources.
2. Key Concepts in Sample Size Calculation
a) One-Tail vs. Two-Tail Tests
The choice between one-tail and two-tail tests depends on your research hypothesis:
- One-tail test: Use when the hypothesis predicts a specific direction (e.g., Drug A is better than Drug B).
- Two-tail test: Use when you’re testing for any difference, regardless of direction (e.g., Drug A has a different effect than Drug B).
b) One Variable vs. Two Variables
- One variable: Use when studying a single characteristic (e.g., prevalence of a disease in a population).
- Two variables: Use when analyzing relationships between two groups or factors (e.g., comparing mean blood pressure between two treatment groups).
Image shows the importance of sample size using a pie chart.
3. When to Use Prevalence vs. Effect Size?
- Prevalence: Use in epidemiological studies to estimate the proportion of a population affected by a condition.
Example: Prevalence of diabetes in a community. - Effect size: Use in interventional or experimental studies to quantify the magnitude of a difference or relationship.
Example: Impact of a new medication on blood pressure reduction compared to a placebo.
4. Sample Size for Pilot or Feasibility Studies
Pilot studies often have smaller sample sizes and focus on feasibility rather than definitive results. General guidelines:
- Use 10-15% of the estimated full sample size for the main study.
- Alternatively, some researchers suggest a range of 10-30 participants per group depending on the complexity of the study.
5. Sample Size for Surveys
For surveys, calculate the sample size using:
- The population size (if known).
- The desired confidence level (e.g., 95%).
- The margin of error (e.g., ±5%).
- The response rate (consider adjustments for low responses).
Online calculators or software like G*Power can simplify this process.
“This video is the property of ‘My easy stats’. We do not own or claim rights to it. Shared here for educational purposes only.”
6. How to Calculate Sample Size Using G*Power
G*Power is a free, user-friendly software widely used for sample size calculation in various study designs. You can download it here: G*Power Official Website.
Features:
- Supports calculations for t-tests, ANOVA, correlation, regression, and more.
- Offers options for one-tailed or two-tailed tests.
- Includes tools for both a priori (before study) and post hoc (after study) calculations.
Hint for Next Blog:
Stay tuned for a detailed guide on different formulae and different variables required for sample size calculation. It will also cover how to use G*Power. The guide includes screenshots and step-by-step instructions to calculate sample sizes for different research designs.
7. Final Tips for Effective Sample Size Calculation
- Define your research question and objectives clearly.
- Consult a statistician when in doubt.
- Use reliable tools like G*Power for precise calculations.
- Pretest with a pilot study to refine your study design.
