Description
Generate integer samples and shuffled exact boxplot summary cards using inclusive median-of-halves quartiles.
A generated practice exercise contains a prompt and an answer key under a specific rule set. A planted answer establishes one possible solution; only an explicit enumeration or uniqueness check establishes whether alternatives exist.
Generate integer samples and shuffled exact boxplot summary cards using inclusive median-of-halves quartiles.
When to use this tool
Use this generator to create reproducible boxplot summary matching exercise 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 sampleCount, sampleSize, minimum, maximum 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 samples, summaries. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.
Draw bounded integer samples, delegate inclusive median-of-halves quartiles and IQR, then shuffle the summary cards while retaining the zero-based sampleIndex answer for each. Return actual sample minimum and maximum with Q1, median, Q3, and IQR.1
Delegated operations used for this result: quartiles; interquartile range. Their calculation or parsing conventions apply to the corresponding result fields.2, 3
| Input field | Meaning and units | Accepted input |
|---|---|---|
| sampleCount | Sample Count | Required. Safe integer; minimum 1; maximum 8. |
| sampleSize | Sample Size | Required. Safe integer; minimum 1; maximum 16. |
| minimum | Minimum | Required. Safe integer. |
| maximum | Maximum | Required. Safe integer. |
| 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 |
|---|---|---|
| samples | Samples | Required. Array of Array of Safe integer.; minimum items 1; maximum items 16.; minimum items 1; maximum items 8. |
| summaries | Summaries | Required. Array of Object; no unknown keys.; minimum items 1; maximum items 8. |
| summaries[].sampleIndex | Sample Index | Required. Safe integer. |
| summaries[].minimum | Minimum | Required. Finite number. |
| summaries[].q1 | Q1 | Required. Finite number. |
| summaries[].median | Median | Required. Finite number. |
| summaries[].q3 | Q3 | Required. Finite number. |
| summaries[].maximum | Maximum | Required. Finite number. |
| summaries[].iqr | Iqr | Required. Finite number. |
| randomness | Replay source and normalized 32-bit seed, or null seed for an advancing context source. | Required. Object; no unknown keys. |
| randomness.seed | Optional safe-integer 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.
{
"input": {
"sampleCount": 1,
"sampleSize": 3,
"minimum": 0,
"maximum": 9,
"seed": 42
},
"output": {
"samples": [
[
6,
4,
8
]
],
"summaries": [
{
"sampleIndex": 0,
"minimum": 4,
"q1": 5,
"median": 6,
"q3": 7,
"maximum": 8,
"iqr": 2
}
],
"randomness": {
"seed": 42,
"source": "seed"
}
}
}Limitations and assumptions
- The displayed extremes are sample extrema, not Tukey whisker fences or outlier-filtered whiskers. Equal summaries can occur. Quartile conventions differ between tools and should be kept explicit when grading.
- A replay requires the same complete input and implementation. The stated sampling convention applies within its eligible space. A finite draw does not exhaust all possible outputs; repeats remain possible unless a distinct-selection rule is explicit.
- 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
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