Freedman's paradox
In statistical analysis, Freedman's paradox, named after David Freedman, is a problem in model selection whereby predictor variables with no relationship to the dependent variable can pass tests of significance – both individually via a t-test, and jointly via an F-test for the significance of the regression. Freedman demonstrated (through simulation and asymptotic calculation) that this is a common occurrence when t
Metadata
- Slug: 00153-freedman-s-paradox
- Type: PARADOX
- Tags: statistics
- Sources: 1
Axioms
- Assume the rules of the domain apply uniformly.
- Assume the observer’s criteria remain fixed.
- Assume classification boundaries stay consistent.
- Assume the model describes the real case.
- Assume repeated steps do not change the outcome.
- Assume no hidden variables are introduced midstream.
Contradictions
- Two reasonable lines of inference yield opposite conclusions
- A global rule conflicts with a local judgment
- A stable resolution appears to violate a starting premise
- Changing the framing reverses the outcome
- Intuition and formalism diverge at the same step
Prompts
- Which assumption is doing the most hidden work?
- What changes if you relax the smallest constraint?
- Does the paradox dissolve or relocate when reframed?
- What is conserved, and what is sacrificed?
Notes
Sources
Overview
In statistical analysis, Freedman’s paradox, named after David Freedman, is a problem in model selection whereby predictor variables with no relationship to the dependent variable can pass tests of significance – both individually via a t-test, and jointly via an F-test for the significance of the regression. Freedman demonstrated (through simulation and asymptotic calculation) that this is a common occurrence when t
Tension
- Two reasonable lines of inference yield opposite conclusions.
- A global rule conflicts with a local judgment.
- A stable resolution appears to violate a starting premise.
- Changing the framing reverses the outcome.
- Intuition and formalism diverge at the same step.
Why It Matters
This entry tests how a stable rule-set can yield unstable conclusions under certain assumptions.
Prompts
- Which assumption is doing the most hidden work?
- What changes if you relax the smallest constraint?
- Does the paradox dissolve or relocate when reframed?
- What is conserved, and what is sacrificed?