Description
Compute bounded observed success-count complements and rates, with null for zero-trial evidence; no inference of failure mechanisms or confidence intervals.
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.
Compute bounded observed success-count complements and rates, with null for zero-trial evidence; no inference of failure mechanisms or confidence intervals.
When to use this tool
Use this tool to compute bounded observed success-count complements and rates, with null for zero-trial evidence; no inference of failure mechanisms or confidence intervals.
How to use the tool and how it works
- Enter successes, totals using the field definitions below.
- Run the operation. Correct input errors before interpreting an output.
- Read result.failures, result.rates. Use the ordering, boundary rules and absence conditions described below.
Require aligned integer successes and totals with successes between zero and total. Return total minus successes and success/total, using null for a zero total.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| successes | Nonnegative integer success counts aligned with totals. | Dense list of at most 128 finite numbers; integer/shape constraints above also apply. |
| totals | Nonnegative integer total counts aligned with successes. | 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.failures | Each total minus its aligned success count. |
| result.rates | Success fractions for each pair, or null where total is zero. |
{
"input": {
"successes": [
2,
0
],
"totals": [
3,
0
]
},
"output": {
"result": {
"failures": [
1,
0
],
"rates": [
0.6666666666666666,
null
]
}
}
}Limitations and assumptions
- Rates are sample fractions, not population probabilities or confidence intervals.
- 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
- Trim Fixed Count per Tail
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- Winsorize Fixed Count per Tail
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