Description
Report nonnegative weight coverage with explicitly known denominator evidence; unknown denominators are not silently treated as zeros or included in ratios.
A descriptive statistic depends on what is counted, how observations are weighted, and which reference or denominator is used. Removing observations differs from replacing them at cutoffs, and a ratio of totals differs from a mean of individual ratios. Keep those conventions and measurement units explicit when comparing results.
Report nonnegative weight coverage with explicitly known denominator evidence; unknown denominators are not silently treated as zeros or included in ratios.
When to use this tool
Use this tool to report nonnegative weight coverage with explicitly known denominator evidence; unknown denominators are not silently treated as zeros or included in ratios.
How to use the tool and how it works
- Enter weights, denominatorKnown using the field definitions below.
- Run the operation. Correct input errors before interpreting an output.
- Read result.totalWeight, result.coveredWeight, result.coverage. Use the ordering, boundary rules and absence conditions described below.
Require aligned nonnegative weights and binary denominatorKnown flags. Sum all weights and the flagged weights; return their ratio, or null when total weight is zero.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| weights | Nonnegative weights aligned with the other list; not automatically normalized. | Dense list of at most 128 finite numbers; integer/shape constraints above also apply. |
| denominatorKnown | Binary 0/1 flags aligned with weights; 1 marks known-denominator coverage. | Dense list of at most 128 finite numbers; integer/shape constraints above also apply. |
| Output field | Interpretation |
|---|---|
| result | Object containing the named fields below. |
| result.totalWeight | Sum of all supplied weights. |
| result.coveredWeight | Sum of weights whose denominatorKnown flag is 1. |
| result.coverage | Covered weight divided by total weight, or null for zero total. |
{
"input": {
"weights": [
2,
3,
5
],
"denominatorKnown": [
1,
0,
1
]
},
"output": {
"result": {
"totalWeight": 10,
"coveredWeight": 7,
"coverage": 0.7
}
}
}Limitations and assumptions
- Coverage is a weighted fraction, not a verification of how denominators became known.
- Use consistent units within each measured list and its reference/cutoffs. Ratios carry numerator units divided by denominator units unless those units cancel. Ordinary floating-point arithmetic is used; input validation does not establish statistical suitability.
- Every declared top-level input field is required and unexpected fields are rejected. Ordinary strings are limited to 8,192 UTF-16 code units and lists to 128 entries, with tighter operation-specific bounds where stated. JSON text has bounded nesting and node counts.
- Numeric outputs must be finite and no larger in absolute magnitude than the safe-integer limit; operations returning exact large integers use decimal text. Positions are zero-based and spans end exclusively unless the operation explicitly states a different convention.
Alternative or Complementary methods
Check the descriptive convention and denominator before comparing results. Use a separate inferential model when you need uncertainty, significance, causal interpretation or population estimates.
References
Similar or alternative tools
- Asymmetric Absolute Deviation Totals
Separate total absolute deviations below and above an explicit reference, retaining asymmetric loss evidence without estimating a distribution.
- Finite Value Sign Counts
Count sign classes of explicitly finite observations, treating signed zero as zero and rejecting nonfinite values rather than dropping them.