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Technical & algorithm documentationSource reviewed

Run descriptive statistics

Summarise distributions, central tendency, variability, frequencies, and cross-tabulations.

When to use this

Use this to understand the sample before testing research claims.

Why it matters

Descriptive evidence reveals coding problems and provides context for every later model.

Step-by-step instructions

1. Open Analysis and choose Find an analysis.
2. Search for descriptive statistics, or browse Descriptive & Exploratory.
3. Confirm the active project, dataset, and immutable version.
4. Review the automatically generated preview and its missing-data handling.
5. Adjust contextual settings; temporary results refresh without creating a research record.
6. Save an immutable analysis only after the evidence is ready for history and reporting.
7. Open Results & Reports to reuse the saved analysis in a Results Notebook.

Expected outputs

Version-bound tables, charts, diagnostics, and reproducibility metadata.

How to interpret the results

Describe the observed sample and distribution. Avoid turning descriptive differences into causal or population claims.

Common mistakes

Do not ignore missing denominators. Do not report excessive decimal precision or treat a mean as sufficient for every variable.

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