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Sample Size Calculator — Planning Checklist + Quick Formulas — GetCalcMaster

Sample size planning guide with quick formulas for means and proportions. Links to power analysis, effect sizes, and multiple comparisons.

Sample size isn’t one number—it depends on your goal (estimation vs testing), your noise level, your acceptable error, and how many comparisons you’ll make. This page gives a practical planning checklist.

Open Statistics Calculator Use Notebook Guides

Important: Educational. Always match the sample size formula to your study design and estimator/test.

What this calculator is

The Statistics Calculator is an interactive tool inside GetCalcMaster. It’s designed to help you explore scenarios, understand formulas, and document assumptions.

Key features

Separates estimation (CI width) from hypothesis testing (power)
Highlights the role of variability and effect size
Includes quick mean/proportion formulas with assumptions
Calls out multiple-comparisons inflation

Formula

Estimation (mean, known/assumed σ):   n ≈ (z * σ / E)^2
Estimation (proportion, p estimate): n ≈ z^2 * p(1-p) / E^2
Testing often uses power formulas and is design-specific.

Quick examples

If you want a 95% CI with margin of error E, increasing confidence raises z and increases n.
For proportions, worst-case variance is at p=0.5 → largest n for fixed E.
For tests, shrinking MDE (effect size) increases required n roughly like 1/effect².

How to use it (quick steps)

Decide: estimation (margin of error) or hypothesis testing (power).
Choose confidence level / α and target power (if testing).
Estimate variability (σ for means, p for proportions) from prior data.
Choose a practical margin of error or effect size.
Compute n, then adjust for design effects, missingness, and multiple comparisons.