Trim Explicit Cutoffs

Description

Trim observations strictly outside supplied numeric cutoffs, preserving original order and removal provenance; no quantile estimation.

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.

Trim observations strictly outside supplied numeric cutoffs, preserving original order and removal provenance; no quantile estimation.

When to use this tool

Use this tool to trim observations strictly outside supplied numeric cutoffs, preserving original order and removal provenance; no quantile estimation.

How to use the tool and how it works

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

Keep observations within the inclusive supplied cutoffs in original order and return removed original indexes.^[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.
minimum	Inclusive lower cutoff or mapped-range endpoint, in the values’ units.	A finite number satisfying the operation’s stated comparisons.
maximum	Inclusive upper cutoff or mapped-range endpoint, in the values’ units.	A finite number satisfying the operation’s stated comparisons.

Output field	Interpretation
result	Object containing the named fields below.
result.values	Transformed or retained observations in the ordering described above.
result.removed	Original indexes of observations outside the retained cutoff interval.

json

{
  "input": {
    "values": [
      0,
      2,
      9
    ],
    "minimum": 1,
    "maximum": 5
  },
  "output": {
    "result": {
      "values": [
        2
      ],
      "removed": [
        0,
        2
      ]
    }
  }
}

Limitations and assumptions

This uses numeric cutoffs, not a fixed number or percentage of observations.
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: Trim Explicit Cutoffs — tool.io

Similar or alternative tools

Winsorize Explicit Cutoffs
Winsorize finite observations at explicit inclusive numeric cutoffs, retaining original changed indexes; does not estimate population tail cutoffs.
Trim Fixed Count per Tail
Apply fixed-count-per-tail sorted trimming with a nonempty retained center; exact equal values use stable numeric order.