Calculator Inputs
Formula Used
Population proportion: n₀ = Z²p(1 − p) ÷ e².
Finite population: n = n₀ ÷ [1 + (n₀ − 1) ÷ N].
Population mean: n = (Zσ ÷ e)².
Accuracy precision: n = Z²a(1 − a) ÷ e².
Collection target: usable samples ÷ (1 − expected loss rate).
ML recommendation: the calculator compares applicable statistical and heuristic estimates, then uses the largest planning value.
How to Use
- Select the calculation mode matching your project.
- Enter confidence, precision, and population assumptions.
- Add model complexity and class-balance details.
- Choose training, validation, and testing percentages.
- Estimate missing, invalid, duplicate, or filtered records.
- Calculate and review the largest recommended estimate.
- Export the plan using copy, CSV, or PDF controls.
Example Dataset Plans
| Project | Classes or predictors | Typical split | Primary planning concern |
|---|---|---|---|
| Binary fraud classification | 2 classes, 40 features | 70 / 15 / 15 | Minority-class evaluation size |
| Image recognition | 10 classes, neural model | 80 / 10 / 10 | Samples per class and augmentation |
| Tabular regression | 18 predictors | 70 / 15 / 15 | Predictor count and expected R² |
| Customer survey estimate | Finite population | Not applicable | Confidence and margin of error |
| A/B experiment | 2 groups | Experiment allocation | Power, significance, and effect size |
Important Planning Guidance
Dataset size affects model stability, uncertainty, and generalization. Larger datasets often reduce variance and improve evaluation reliability. Data quality still determines whether added observations provide useful information.
Statistical formulas estimate precision under explicit assumptions. Machine learning heuristics consider classes, features, parameters, and batches. Neither approach guarantees a particular production accuracy or business outcome.
Validation and testing data must represent future observations. Prevent leakage between related records, users, or time periods. Use group-aware or time-aware splitting whenever random splitting is inappropriate.
Class imbalance changes the useful sample requirement substantially. Overall accuracy may hide weak minority-class performance. Review precision, recall, specificity, calibration, and per-class confidence intervals.
Frequently Asked Questions
What sample-size method should I choose?
Choose the mode matching your main decision. Classification planning emphasizes classes, accuracy, features, and batches. Statistical modes emphasize confidence, precision, power, and effect size.
Why does the calculator use the largest estimate?
Each method protects against a different limitation. The largest applicable estimate provides a conservative planning baseline. You should still validate assumptions with learning curves.
Does more data always improve a model?
No, duplicated or biased observations may add little information. Label quality and population coverage remain important. Model architecture and preprocessing also affect performance.
How should I handle class imbalance?
Plan enough minority examples for every data split. Consider class weights, targeted collection, or careful resampling. Evaluate minority performance using appropriate class-level metrics.
Can augmentation replace real observations?
Augmentation can improve training diversity under suitable transformations. It does not create fully independent evaluation evidence. Keep validation and testing sets based on real observations.
What confidence level is commonly used?
Ninety-five percent is a common general-purpose choice. Higher confidence usually requires a larger sample. Select confidence based on decision risk and domain standards.
Why adjust for missing or invalid data?
Some collected records will not remain usable. Ignoring losses can leave the final dataset undersized. The collection target compensates for expected attrition.
How many cross-validation folds should I use?
Five or ten folds are common starting points. Smaller datasets may benefit from more folds. Large datasets often need fewer folds for efficiency.
Is this calculator a guarantee of model accuracy?
No sample-size calculator can guarantee model performance. Results depend on signal strength, drift, leakage, labels, and algorithms. Treat every estimate as a documented planning assumption.