The Researcher’s Standard for Sample Size Determination.
Don’t let an underpowered study invalidate your hard work. Calculate exactly how many subjects you need to achieve statistical significance—whether for a simple survey or a complex clinical trial.
📊 Sample Size Calculator
Calculate appropriate sample sizes for clinical trials and research studies using statistical power analysis.
🤖 AI-Powered Study Design
Calculate required sample sizes with AI recommendations for study design, statistical power, and practical considerations.
Safety Disclaimer
Clinical Decision Support Only This tool is an aid for healthcare professionals. It does not replace clinical judgment. Always verify critical interactions with official FDA/EMA labeling.
Core Capabilities
From Population Surveys to Clinical Trials
- 📊 Standard Survey ModeIdeal for cross-sectional studies and prevalence surveys. Input your Population Size, Confidence Level, and desired Margin of Error to get your target “N” instantly.
- 🔬 Clinical Research ModeGo beyond basic surveys. Adjust for Statistical Power ($1-\beta$) and Alpha Levels ($\alpha$) to ensure your randomized controlled trial (RCT) detects a true effect if one exists.
- 📉 Finite Population CorrectionWorking with a small, known group (e.g., “Patients with Rare Disease X in Hospital Y”)? Our calculator applies the FPC factor automatically to prevent over-sampling.
- ⚠️ Dropout BufferReal-world research has attrition. The tool suggests an “Adjusted Sample Size” based on your anticipated dropout rate (e.g., +10% or +20%).
The “Why” (SEO Content Block)
Why Precision Matters in Medical Research
Avoid “Underpowered” Studies
A study with too few participants may fail to detect a significant difference between a drug and a placebo, leading to false negatives (Type II errors).1 Our power analysis tool helps you avoid this costly mistake.
Ethical Resource Management
In medicine, recruiting more patients than necessary is unethical; recruiting too few is wasteful. [Your Tool Name] helps you find the “Goldilocks” number—the minimum sample size required to prove your hypothesis scientifically.
Support for Multiple Study Designs
Whether you are calculating for proportions (e.g., % of patients cured) or means (e.g., average reduction in blood pressure), our engine handles the specific statistical formulas for each.
Educational Resources
Understanding the Variables
- Confidence Level: How sure you want to be that the actual results fall within your range (Industry standard: 95% or 99%).
- Margin of Error: The amount of random sampling error you are willing to accept (typically ±5%).
- Population Proportion: The expected prevalence of the outcome. If unknown, we default to 50% (the most conservative estimate).
Use Cases
Built for Every Stage of Research
- Medical Students: Justifying sample size for a thesis or dissertation.
- Public Health Officials: Planning epidemiological surveys for disease prevalence.
- Principal Investigators: Writing grant proposals that require robust statistical methodology.
Frequently Asked Questions
Statistics FAQ
Q: What if I don’t know the population size?
A: That’s fine! For large populations (e.g., “All adults in the USA”), the population size becomes mathematically irrelevant. Leave it blank, and the calculator assumes an infinite population.
Q: Why is 50% used as the default proportion?
A: In statistics, a proportion of 50% yields the largest sample size (maximum variance). Using 50% ensures your sample is large enough regardless of the actual distribution, keeping your study safe.
Q: Can I export the calculation for my protocol?
A: Yes. You can download a PDF summary that states the formula used (e.g., Cochran’s Formula) and the parameters input, ready to be attached to your IRB or Ethics Committee application.
