Explicit Robust Cutoff Classification Table

Description

Classify observations against explicitly supplied inclusive robust-screen cutoffs; does not estimate robust cutoffs or establish outliers, errors or diagnosis.

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.

Classify observations against explicitly supplied inclusive robust-screen cutoffs; does not estimate robust cutoffs or establish outliers, errors or diagnosis.

When to use this tool

Use this tool to classify observations against explicitly supplied inclusive robust-screen cutoffs; does not estimate robust cutoffs or establish outliers, errors or diagnosis.

How to use the tool and how it works

Enter values, lower, upper using the field definitions below.
Run the operation. Correct input errors before interpreting an output.
Read result.classes. Use the ordering, boundary rules and absence conditions described below.

Require upper at least lower. Label each value below, above or within using strict outside comparisons and inclusive inside boundaries.^[1]

Input field	Meaning and units	Accepted input
values	Finite observations in the measurement units used by reference or cutoffs.	Dense list of at most 128 finite numbers; integer/shape constraints above also apply.
lower	Inclusive lower classification boundary in the values’ units.	A finite number satisfying the operation’s stated comparisons.
upper	Inclusive upper classification boundary in the values’ units.	A finite number satisfying the operation’s stated comparisons.

Output field	Interpretation
result	Object containing the named fields below.
result.classes	One below, within or above label for each original observation.

json

{
  "input": {
    "values": [
      0,
      2,
      4
    ],
    "lower": 1,
    "upper": 3
  },
  "output": {
    "result": {
      "classes": [
        "below",
        "within",
        "above"
      ]
    }
  }
}

Limitations and assumptions

The tool does not estimate robust cutoffs or infer outliers from the data.
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

Tool behavior and example: Explicit Robust Cutoff Classification Table — tool.io

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.