Sample Count for Estimating Proportion
Plan binary-trial sample count using expected proportion and error margin.
Your numbers
Sample count wey you need
Normal approximation; use 50% as conservative assumption if proportion unknown. Cluster sampling need separate design adjustment.
Compare scenarios
You fit keep up to three snapshots for this tab. Loading one go replace the present inputs.
Formula
n₀=z²p(1−p)/E²; you fit choose to apply finite-population correction.
Plan binary-trial sample count using expected proportion and error margin.
Example wey show the calculation
- Proportion wey you expect (%)
- 50
- Error margin wey you want (percentage points)
- 5
- Di confidence level (%)
- 95
- Finite population count; zero mean unlimited
- 0
- Sample count wey you need
- 385
Where e fit work and limits
Normal approximation; use 50% as conservative assumption if proportion unknown. Cluster sampling need separate design adjustment.
We check inputs for missing values and allowed ranges. Result wey pass the finite numeric range no go show. Rounding affect wetin display, while calculation use the model numeric precision.
Check your units and assumptions before you rely on result. Changing label no convert currency or physical unit.
Version of di formula: ef2c3376ca92 · Example don get check: