Calculator Inputs
Use the form below to estimate a precision-based sample size.
Example Data Table
| Use Case | Mode | Confidence | Variability Input | Margin | Population | Estimated Final Sample |
|---|---|---|---|---|---|---|
| Vision quality defect labeling | Binary proportion | 95% | p = 50% | 5% | 10,000 | 370 |
| Sensor latency benchmark study | Continuous variable | 95% | σ = 12 | ±2 units | 5,000 | 136 |
| Fraud review audit sample | Binary proportion | 99% | p = 30% | 4% | 20,000 | 835 |
| Regression target variance check | Continuous variable | 90% | σ = 8 | ±1.5 units | 0 | 78 |
Formula Used
1) Continuous variable sample size
n = (Z × σ / E)²
Use this when the outcome is numeric, such as latency, cost, duration, error magnitude, or a continuous model target. Here, Z is the confidence Z-score, σ is the estimated standard deviation, and E is the allowed margin of error.
2) Binary proportion sample size
n = (Z² × p × (1 − p)) / E²
Use this for yes/no outcomes like defect detection, fraud flags, pass/fail labels, or click/no click events. Here, p is the estimated positive rate and E is the allowed percentage error.
3) Finite population correction
nadj = n / (1 + ((n − 1) / N))
Apply this when the total available dataset is limited. It reduces the sample need when the sample covers a meaningful share of the full population.
4) Design effect and usable-rate adjustment
Final sample = ceil((nadj × design effect) / usable rate)
This step increases the sample if clustering, weighting, label loss, nonresponse, or unusable rows are expected during collection.
How to Use This Calculator
- Choose Continuous variable for measured values, or Binary proportion for yes/no outcomes.
- Select the confidence level. Use a custom Z-score only when your methodology requires one.
- Enter the acceptable margin of error. Continuous mode uses raw units. Proportion mode uses percent.
- Provide the variability input. Use standard deviation for continuous mode, or expected proportion for binary mode.
- Add population size if your data pool is limited. Leave it at 0 to skip that correction.
- Set design effect and usable rate to reflect real collection conditions.
- Use the lean and conservative scenario factors to compare lighter and heavier planning cases.
- Press Calculate Sample Size. Review the result block, chart, and export options.
Why this helps AI & Machine Learning work
Sample planning matters when you are designing dataset audits, labeling budgets, regression studies, monitoring checks, fairness reviews, survey-backed models, and post-deployment quality assessments. This calculator gives a fast planning estimate, then adds practical corrections for finite datasets, response loss, and collection complexity.
FAQs
1) What does this calculator estimate?
It estimates the minimum records needed to measure a numeric variable or binary outcome within a chosen confidence level and acceptable error. It also adjusts for finite populations, design effects, and expected nonresponse.
2) When should I use continuous mode?
Use continuous mode when the target is a measured value like latency, price, temperature, dwell time, or loss. Enter an estimated standard deviation from historical data, a pilot study, or expert judgment.
3) When should I use proportion mode?
Use proportion mode for yes/no, defect/not defect, fraud/not fraud, or click/no click outcomes. Enter the expected positive rate. If you are unsure, use 50% for the most conservative sample size.
4) Why does population size matter?
Population size matters when the total pool is not huge. Finite population correction reduces the needed sample because each selected record represents a larger share of the overall dataset.
5) What is design effect?
Design effect inflates the sample when clustering, stratification imbalance, or weighted sampling reduces efficiency. Simple random sampling often uses 1.0. More complex collection plans may need 1.1 to 2.0 or higher.
6) Why adjust for usable rate?
If you expect missing labels, unusable rows, or survey drop-off, usable-rate adjustment protects the final usable sample. For example, an 80% usable rate means you should recruit more records than the minimum analytic need.
7) Is this a power analysis?
No. This page estimates sample size for precision, not hypothesis testing power. For A/B tests, model comparisons, or effect detection, use a power analysis based on expected effect size and false-positive control.
8) Can I use this for AI projects?
Yes. It is useful for labeling plans, dataset audits, feature studies, error reviews, monitoring checks, and survey-backed ML projects. Treat the result as a planning estimate, then refine inputs with pilot data when available.