Statistical independence test
A statistical independence test is a hypothesis-testing procedure that evaluates whether variables are statistically independent or conditionally independent based on observed data.
Causal AI
A statistical independence test is a hypothesis-testing procedure that evaluates whether variables are statistically independent or conditionally independent from observed data.
- How it is used
- Checks whether observed data contradict the conditional independences implied by a proposed causal DAG.
- How deep it goes
- Defines the statistic and p-value, works through pgmpy testing, and examines errors and sample-size sensitivity without developing new tests.
- Examples
- Uses a transportation survey’s education–transport claim, a pgmpy chi-squared call, and bootstrap plots of sample-size sensitivity.
- What you can do after
- Apply it via pgmpy to check DAG-implied independences and treat failures as prompts to revisit the model.
Effective Data Analysis
An independence test is a chi-square test that evaluates whether the distributions of two categorical variables are associated.
- How it is used
- Uses categorical counts or proportions to evaluate association and detect disproportionality against expected frequencies.
- 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 you can do after
- Apply it via SciPy to calculate chi-square values, p-values, and expected frequencies.
Read how Effective Data Analysis explains statistical independence test →
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.
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