Common Language Effect Size Calculator
Empirical commonlanguageeffectP(X>Y)+.5ties across all observed independent pair comparisons. Higher direction explicit; not Gaussian assumption or clinical norm. Actual independently qualified instrument/cohort/matching units; no diagnosis, causal outcome, validated personal prediction or universal clinical cutoff.
Description
Common-language effect size summarizes cross-group pairwise superiority.
Common-language effect size summarizes cross-group pairwise superiority.1
Giving ties half weight makes the direction and tie convention explicit. The calculation and input requirements are explained below.
When to use Common Language Effect Size Calculator
Enter two nonempty observation lists with at most 1000000 pairs.
How to use Common Language Effect Size Calculator and how it works
- Prepare the measurements or independently established model inputs described below, with matching units and observation scope.
- Enter the required fields and run Common Language Effect Size Calculator.
- Read the result fields using the formula, output units, signs and ordering explained below.
- Correct any rejected inputs and rerun; interpret the result within the stated model and its assumptions.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| actualFirstGroupObservations | Actual First Group Observations. Use the units and matching context stated in the calculation below. | array of number | Required; other constraints below |
| actualSecondGroupObservations | Actual Second Group Observations. Use the units and matching context stated in the calculation below. | array of number | Required; other constraints below |
Enter two nonempty observation lists with at most 1000000 pairs. Give each pair one when first is higher, half when tied and zero when lower. Return the average empirical superiority fraction.
commonLanguage=(wins+0.5*ties)/pairCount Apply the input ordering, units and branch conditions stated above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Object containing the calculated or selected fields below. | object |
| result.value | Numeric result under the formula and output units explained above. | number |
{
"input": {
"actualFirstGroupObservations": [
2,
3
],
"actualSecondGroupObservations": [
1,
3
]
},
"output": {
"result": {
"value": 0.625
}
}
}Limitations and assumptions
The calculation uses the supplied observations and model inputs. It does not collect measurements, select a reference population or coefficient, or establish that the model applies to the actual case. The stated branch conditions and output units remain part of the result.
All fields require explicit values. Unexpected fields, wrong types, nonfinite numbers and sparse arrays are rejected. Alignment, bounds and supported-token rules are explained above; extremely large inputs or nonfinite results can also fail. Input errors differ from successful false comparisons, unavailable-domain flags and empty match lists.
Alternative or Complementary methods
Use Cliff Delta Nonoverlap Calculator for a complementary operation: Cliff's delta compares all cross-group observation pairs.
Use Hedges g From t Statistic Converter for a complementary operation: Hedges' g adjusts an independent-group standardized contrast for sample-size bias.
References
-
Common Language Effect Size Calculator — tool.io
Similar or alternative tools
- Cliff Delta Nonoverlap Calculator
Cliff's delta compares all cross-group observation pairs.
- Hedges g From t Statistic Converter
Hedges' g adjusts an independent-group standardized contrast for sample-size bias.