How to use this calculator
- Select a confidence level.
- Choose an acceptable margin of error.
- Enter the expected proportion or use 50% conservatively.
- Enter the eligible population, or 0 if it is large or unknown.
#3164 · AI & Technology Tool
Analytics Team Sample Size Calculator
Estimate how many analytics work items should be reviewed to measure a pass rate with a chosen confidence level and margin of error. The calculation uses the expected pass proportion and applies a finite-population correction when a known eligible population is entered. It is designed for estimating one proportion, not for proving that two teams differ. A representative selection process remains essential even when the numerical sample target is met.
Calculator
Precision targetFormula
n₀ = z² × p × (1 − p) ÷ e²
For known population N: n = n₀ ÷ (1 + (n₀ − 1) ÷ N). The final result is rounded up.
What the result means
The result is the minimum completed, usable sample under simple random sampling assumptions. It does not repair selection bias or incomplete measurement.
Plan additional outreach or collection when some sampled observations may be missing, ineligible, or unusable.
Example calculation
At 95% confidence, a ±5-point margin, 50% expected pass rate, and population of 1,000, the adjusted requirement is 278 reviewed items; the large-population requirement is 385.
Tips for better results
- Use 50% when the expected rate is unknown.
- Define the eligible population before sampling.
- Sample without convenient-only selection.
- Allow extra volume for missing observations.
- Choose margin of error based on the decision at hand.
Frequently asked questions
Why does a 50% expected proportion require the largest sample?
For a binary proportion, variability is greatest at 50%, so it produces the most conservative requirement when the true rate is uncertain.
What does entering zero for population mean?
It tells the calculator to use the large or unknown population formula without a finite-population correction.
Does this sample size account for nonresponse or missing telemetry?
No. Increase the outreach or eligible collection target to allow for expected nonresponse, exclusions, or unusable observations.
Can I use this result to compare two groups?
Not directly. This formula estimates one proportion; a two-group comparison needs assumptions about both rates, allocation, significance, and power.
Why does a smaller margin of error increase the sample?
Tighter precision requires more information. Sample size grows approximately with the inverse square of the margin of error.
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Sample-size inputs
| Symbol | Meaning |
|---|---|
| z | Confidence z-score |
| p | Expected proportion |
| e | Margin of error |
| N | Finite population, if known |