Random Stratified Row Sample Generator

Description

Sample supplied records under exact stratum quotas and replacement rules, retaining row inclusion probabilities and observed draw counts.

Sampling selects from a supplied population under rules for replacement, grouping, or eligibility. Draw probabilities, inclusion probabilities, and repeat counts describe different aspects of selection; a reproducible sample is not automatically representative of a real population.

Sample supplied records under exact stratum quotas and replacement rules, retaining row inclusion probabilities and observed draw counts.

When to use this tool

Use this generator to create reproducible stratified row sample cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply rows, quotas in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read draws, inclusion. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Sample each requested stratum independently under its quota and replacement policy, retaining source row indexes and per-row draw counts. Inclusion probability is k/N without replacement and 1-(1-1/N)^k with replacement; unrequested strata have probability zero.^[1]

Input field	Meaning and units	Accepted input
rows	Rows	Required. Array of Object; no unknown keys.; minimum items 0; maximum items 512.
rows[].id	Literal identifier used to match records, independently of their display labels.	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
rows[].values	Values	Required. Array of Text; maximum UTF-16 length 4096.; minimum items 0; maximum items 32.
rows[].stratum	Stratum	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
quotas	Quotas	Required. Array of Object; no unknown keys.; minimum items 0; maximum items 64.
quotas[].stratum	Stratum	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
quotas[].count	Requested count; the mechanics explain whether one draw expands into several returned records.	Required. Safe integer; minimum 0; maximum 4096.
quotas[].replacement	Replacement	Required. Boolean.
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
draws	Draws	Required. Array of Object; no unknown keys.; minimum items 0; maximum items 4096.
draws[].sourceIndex	Source Index	Required. Safe integer; minimum 0.
draws[].row	Row	Required. Object; no unknown keys.
draws[].row.id	Literal identifier used to match records, independently of their display labels.	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
draws[].row.values	Values	Required. Array of Text; maximum UTF-16 length 4096.; minimum items 0; maximum items 32.
draws[].row.stratum	Stratum	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
inclusion	Inclusion	Required. Array of Object; no unknown keys.; minimum items 0; maximum items 512.
inclusion[].rowId	Row Id	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
inclusion[].stratum	Stratum	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
inclusion[].population	Population	Required. Safe integer; minimum 0.
inclusion[].quota	Quota	Required. Safe integer; minimum 0.
inclusion[].draws	Draws	Required. Safe integer; minimum 0.
inclusion[].probability	Probability	Required. Finite number; minimum 0; maximum 1.
randomness	Replay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source.	Required. Object; no unknown keys.
randomness.seed	Optional numeric replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "rows": [
      {
        "id": "n1",
        "stratum": "north",
        "values": [
          "oak"
        ]
      },
      {
        "id": "n2",
        "stratum": "north",
        "values": [
          "pine"
        ]
      },
      {
        "id": "s1",
        "stratum": "south",
        "values": [
          "elm"
        ]
      }
    ],
    "quotas": [
      {
        "stratum": "north",
        "count": 1,
        "replacement": false
      },
      {
        "stratum": "south",
        "count": 1,
        "replacement": false
      }
    ],
    "seed": 42
  },
  "output": {
    "draws": [
      {
        "sourceIndex": 1,
        "row": {
          "id": "n2",
          "values": [
            "pine"
          ],
          "stratum": "north"
        }
      },
      {
        "sourceIndex": 2,
        "row": {
          "id": "s1",
          "values": [
            "elm"
          ],
          "stratum": "south"
        }
      }
    ],
    "inclusion": [
      {
        "rowId": "n1",
        "stratum": "north",
        "population": 2,
        "quota": 1,
        "draws": 0,
        "probability": 0.5
      },
      {
        "rowId": "n2",
        "stratum": "north",
        "population": 2,
        "quota": 1,
        "draws": 1,
        "probability": 0.5
      },
      {
        "rowId": "s1",
        "stratum": "south",
        "population": 1,
        "quota": 1,
        "draws": 1,
        "probability": 1
      }
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Row IDs and quota strata must be unique. Without-replacement quotas cannot exceed their pools, and draws require eligible rows. Inclusion probability is not a count weight or a population estimate.
A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random Stratified Row Sample Generator — tool.io

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