Sten Score From Z Converter
Declared sten round half up(5.5+2z) clamped1–10convention, not all publisher norm tables or unboundedTscore. Actual independently qualified instrument/cohort/matching units; no diagnosis, causal outcome, validated personal prediction or universal clinical cutoff.
Description
Sten scores group standardized observations into ten categories under a stated rounding convention.
Sten scores group standardized observations into ten categories under a stated rounding convention.1
Publisher-specific norm tables may use different boundaries. The calculation and input requirements are explained below.
When to use Sten Score From Z Converter
Enter a finite matched z-score.
How to use Sten Score From Z Converter 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 Sten Score From Z Converter.
- 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 |
|---|---|---|---|
| actualMatchedZ | Actual Matched Z. Use the units and matching context stated in the calculation below. | number | Required; finite; other constraints below |
Enter a finite matched z-score. Compute floor(5.5+2z+0.5), then clamp the integer to 1–10. This explicit half-up convention gives z=0 a sten of 6.
sten=clamp(floor(5.5+2*z+0.5),1,10) 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": {
"actualMatchedZ": 0
},
"output": {
"result": {
"value": 6
}
}
}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 Z Composite Average Standardized Tool for a complementary operation: A mean of standardized components combines scores on matched reference scales.
Use T-Score Conversion Mean 50 SD 10 for a complementary operation: A T-score convention maps a matched z-score to a scale centered at 50 with a standard deviation of 10.
References
-
Sten Score From Z Converter — tool.io
Similar or alternative tools
- Z Composite Average Standardized Tool
A mean of standardized components combines scores on matched reference scales.
- T-Score Conversion Mean 50 SD 10
A T-score convention maps a matched z-score to a scale centered at 50 with a standard deviation of 10.