The selector creates a shortlist from the study structure; assumptions still determine whether a method is defensible.
numeric→one sample(s)→independent→candidate test
1EnterProvide the known values
2CalculateResults update automatically
3VerifyReview the details and units
Try an example
Enter plain numbers without measurement units. Datasets accept commas, spaces, semicolons, or line breaks and are limited to 10,000 values. Results stay in this browser.
Candidate method
Calculated resultCalculated from the values currently entered
Understand and verify
Suggested starting point: One-sample t procedure, with design and distribution checks
Required next check: Sampling design, independence, outcome definition, sample size, assumptions, and estimand
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Statistical Test Selector. The outcome scale, number of samples, and dependence structure narrow the options. The scientific estimand and data-generating process make the final choice.
1State the question and estimand2Map the sampling relationship3Check assumptions before calculating
Test statistic and reference model
Method selection depends on the estimand, design, outcome scale, dependence, and model assumptions.
The result is a structured shortlist with checks to perform, not automatic validation of study design.
Responsible interpretation
A selector cannot inspect bias, measurement quality, missingness, confounding, influential observations, or whether the study can answer the question.
Required assumption: No selector can diagnose sampling bias, measurement quality, dependence, confounding, or distributional suitability from labels alone.
Quick guide
How to use this calculator
Enter the observations, probabilities, model parameters, or summary statistics requested by the visible labels.
Keep every value on the same scale and confirm that the selected sampling relationship, distribution, and tail convention match the question you are investigating.
Read the result together with its assumptions and interpretation. Statistical output summarizes uncertainty under a model; it does not repair biased data or establish causation.
Calculation method
How the statistical test selector works
Method selection depends on the estimand, design, outcome scale, dependence, and model assumptions.
The result is a structured shortlist with checks to perform, not automatic validation of study design.
Worked example
Statistical Test Selector example
Two independent numeric groups suggest Welch’s two-sample t procedure when its conditions are defensible.
Method selection depends on the estimand, design, outcome scale, dependence, and model assumptions.
Supported inputs
Precision and limits
Model and design
No selector can diagnose sampling bias, measurement quality, dependence, confounding, or distributional suitability from labels alone.
Numerical scope
Inputs use double-precision numerical methods with guarded domains. Datasets accept up to 10,000 finite plain-decimal values. Extremely large parameters or probabilities deep in a numerical tail may require specialist statistical software.
Interpretation
The result is a structured shortlist with checks to perform, not automatic validation of study design.
Decision boundary
The calculator does not validate how data were collected, diagnose dependence or bias, choose a scientifically meaningful effect, or replace review by a qualified statistician for consequential research, medical, regulatory, safety, or policy decisions.
Privacy
Entered values and calculated results stay in this browser and are not sent to an analytics service.
