Methodology

Public method record

This page is the public record for methodology version 1.2. It describes what the current publication checks and what readers can reproduce; it is not a claim of independent certification.

Method record

Version 1.2

Publication build

ertaiba-20260828T094508Z

Calculation checks

Seed replay, fixed examples, valid parameter bounds, mathematical invariants, and edge conditions.

Publishing checks

Content metadata, canonical URLs, structured data, internal routes, and production output.

Browser checks

Interactive controls, value tables, keyboard paths, mobile layout, and no-JavaScript fallback.

One question per experiment

Each lab begins with a misconception that can be tested by changing a small number of assumptions. The model is deliberately narrower than a real study. That constraint helps a reader see the mechanism, but it also means the result must not be exported into a real-world conclusion without a suitable design and data.

Parameter ranges are bounded to values that keep the display interpretable and computation responsive. A control changes the model described on the page; it does not query an external database or reveal a hidden real-world estimate.

Deterministic random draws

Random-looking examples use a text seed. The seed is hashed into a 32-bit state and passed to a deterministic pseudorandom generator. Equal seeds produce equal streams in supported browsers. Normal draws use the Box-Muller transform. Right-skewed examples exponentiate normal draws to form a log-normal teaching population.

The generator is suitable for reproducible illustrations. It is not a cryptographic generator, a security test, or a claim that statistical randomness can be proved from a visual pattern. Selecting New draw changes the seed; moving a parameter keeps the current seed so the effect of that parameter is easier to compare.

Calculations

Core summaries use the open-source simple-statistics project where an established implementation fits the task. Site-specific functions compose those primitives for repeated sample means, Tukey IQR fences, known-sigma z intervals, natural-frequency Bayes counts, weighted group rates, percentage changes, correlation examples, paired regression-to-the-mean simulations, biased response mixtures, binary run lengths, axis-scale comparisons, and families of null-hypothesis tests.

Unit tests check deterministic output and mathematical invariants. Examples include: histogram counts must add to the source count; a higher base rate must increase the expected true share among otherwise identical alerts; aggregate rates must equal success totals divided by case totals; selected retest means should move inward in the fixed regression model; a representative response mixture must recover the population target; run counts must match the displayed sequence; axis scaling must leave data values unchanged; and a Bonferroni threshold must equal the family alpha divided by the test count.

A worked input and output

The percentage-change calculation offers a compact fixed check. For an input of 40% before and 45% after, the site-specific function returns a change of 5 percentage points and a relative change of 12.5%. These values are different quantities even though they use the same inputs.

Open the same fixed example in the interactive lab, then compare the headline metrics with the value table. The controls are stored in the URL, so the same link restores the same non-random input.

Source availability and verification

The public reproducibility kit contains the versioned pure calculation source, 17 deterministic tests, the exact calculation dependency, plain-text run instructions, and SHA-256 checksums. The downloadable test file imports the downloadable source directly, so readers can rerun the checks without relying on this website after download.

The full deployment repository and account configuration remain private. Publishing the calculation snapshot makes the model inspectable; it is not an independent source-code audit, security review, or open-source license. Readers can also check public behavior through fixed parameter links, deterministic seeds, exact value tables, CSV exports, formulas in the paired guides, the source index, and dated correction records.

Questions about a calculation, an edge case, or a reproducibility failure can be sent to editorial@ertaiba.com with the publication revision, experiment URL, displayed seed, and command output when available.

What the charts show

Charts are generated in the browser from the same result object used to create the metrics and value table. This avoids presenting a decorative picture unrelated to the stated calculation. Color is never the only carrier of meaning: labels, counts, position, captions, and tables provide additional cues.

Some figures show only a subset of repeated values so individual marks remain legible. The caption states when this happens. Aggregate counts still use the full simulation.

Editorial workflow

1. Define the question, assumptions, and prohibited interpretations.
2. Implement the pure calculation and deterministic generator.
3. Add tests for fixed examples, edge conditions, and reproducibility.
4. Write an original guide around a synthetic scenario.
5. Check statistical definitions against primary teaching or standards sources.
6. Review mobile, keyboard, reduced-motion, and no-JavaScript behavior.
7. Run content, link, accessibility, and build-output checks.

Limits

A correct simulation can still answer the wrong question. The labs do not repair biased samples, define causal estimands, validate measurement, or choose a decision threshold. They are designed to make those missing choices harder to overlook. Readers applying statistics to consequential work should use methods and review appropriate to their field.