Set confidence, margin, deviation, and population assumptions. Review base, corrected, and adjusted sample counts instantly. Export results, compare scenarios, and justify stronger study planning.
These example scenarios illustrate how population, precision, and field assumptions affect the final invite count.
| Scenario | Confidence | Type | Std. Dev. | Margin | Population | DEFF | Nonresponse | Final Invite n |
|---|---|---|---|---|---|---|---|---|
| Customer satisfaction pilot | 95% | Two-sided | 12 | 2 | Large | 1.00 | 0% | 139 |
| Employee pulse survey | 95% | Two-sided | 18 | 3 | 2,500 | 1.20 | 10% | 175 |
| Clinical mean estimate | 99% | Two-sided | 25 | 4 | 800 | 1.50 | 15% | 346 |
Base sample size for a mean: n0 = (Z × σ / E)²
Finite population correction: n = n0 / (1 + ((n0 - 1) / N))
Design adjustment: n_design = n × DEFF
Nonresponse adjustment: n_final = n_design / (1 - r)
Relative precision: (E / mean) × 100%, when an expected mean is provided.
Here, Z is the critical value, σ is the estimated standard deviation, E is the allowed half-width of error, N is the population, DEFF is the design effect, and r is the anticipated nonresponse rate.
It estimates how many observations or invitations you need to estimate a population mean within a chosen margin of error and confidence level.
Enter population size when the total eligible group is limited and known. That enables finite population correction, which can reduce the required sample.
Standard deviation measures variability. More variability means you need more observations to estimate the mean with the same precision.
A tighter margin demands more precision. Because sample size is inversely related to the square of the margin, small error reductions can raise sample requirements sharply.
Design effect adjusts for complex sampling, such as clustering or weighting. A value above one inflates the sample size needed versus simple random sampling.
Not everyone responds or provides usable data. Inflating the invite count helps you still achieve the number of completed observations your study needs.
Two-sided settings are common for estimation because they protect both directions of error. One-sided settings are more specialized and usually need strong justification.
Yes. Pilot estimates are often practical starting points. When uncertainty is high, use a conservative deviation so your final sample is less likely to be underpowered.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.