Statistical independence test

As explained in Effective Data Analysis

An independence test is a chi-square test that evaluates whether the distributions of two categorical variables are associated.

How Effective Data Analysis covers it

How deep it goes

Explains contingency tables, expected frequencies, sample-size sensitivity, heatmaps, and pairwise follow-up tests.

Examples

Uses recommender versions, countries, feature engagement, click counts, expected-frequency tables, and difference heatmaps.

What it assumes

Assumes categorical data and frequency tables while defining observed-versus-expected comparison through examples.

What you can do after

Apply it via SciPy to calculate chi-square values, p-values, and expected frequencies.

How it connects

Connects categorical variables with contingency tables, goodness-of-fit tests, trend tests, and chi-square testing.

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Also explained in

• 

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  Causal AIA statistical independence test is a hypothesis-testing procedure that evaluates whether variables are statistically independent or conditionally independent from observed data.

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Related concepts

• latent variablecontrasts with · A latent variable is an unobserved quantity represented or inferred by a model to capture underlying structure in observed data.
• d-separationused by · D-separation is a graphical criterion for determining whether a conditioning set blocks all relevant paths between variables in a graph, implying conditional independence under the graph’s assumptions.
• p-valueproduces · A p-value is the probability, assuming a specified null hypothesis, of obtaining a test statistic or result at least as extreme as the observed one.
• conditional independencemeasures · Two variables are conditionally independent given specified information when their conditional joint distribution factors into the product of their conditional distributions.
• thresholdconfigured by · A threshold is a boundary value against which a quantity is compared to determine whether a condition is met or an action or decision is triggered.

This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.