Patterns Pattern · cautionary-bad

Garden of forking paths (researcher degrees of freedom / undeclared analysis family)

Stub. Corpus-first: starts minimal, grows only when a real case demands it (content/meta/guiding-principles.md). The boundary table below is the load-bearing content — this leaf exists mainly to be distinguished from its siblings. Each signature is added only when its primary is read into a content/research/papers/<id>/ note. Frontmatter contract for patterns deferred to repo standup.

Phenomenon (one line): analytic choices — which comparison, which subgroup, which covariates, which exclusions, which transformation, which outcome coding — are made contingent on the observed data, so the reported test is one of many implicit forks that could have been taken. The p-value is computed as if the analysis were pre-specified; it is not, so it lies. (term: Gelman & Loken 2013/2014; “researcher degrees of freedom”: Simmons, Nelson & Simonsohn 2011.)

The load-bearing subtlety (why this is not just “multiple testing”). Error inflates EVEN WHEN

  • only a single test is actually run (no fishing, no p-hacking), AND
  • the research hypothesis was posited ahead of time

— because under different data a different fork would have been chosen. The multiplicity is latent (the paths not taken), not manifest. This is Gelman & Loken’s central claim and the whole reason the term exists; do not reduce it to “they ran too many tests.”

Boundary — vs the sibling leaves (draw this sharply; they genuinely overlap)

LeafMechanismDetector
garden-of-forking-paths (this)analysis specification chosen post-hoc; test family undeclared. Latent multiplicity even at N=1 test.provenance: were the comparisons / subgroups / covariates / exclusions / outcome pre-specified before seeing data?
double-dippinga specific structural circularity: the same data selects a feature then tests it; non-independence under the nullstructural: is there a selection step feeding a test on the same data?
p-hacking / fishingmanifest multiplicity: many analyses run, the best reportedmany tests + selective reporting
  • double-dipping ⊂ forking-paths conceptually (a data-contingent selection is one fork), but it is sharper and structurally detectable, so it earns its own leaf. This leaf is the residual: post-hoc choices that are not a single clean select-then-test — subgroup fishing, covariate/model selection, outcome switching, flexible exclusions.
  • forking-paths ≠ p-hacking — the working-paper title says so verbatim (“even when there is no ‘fishing expedition’ or ‘p-hacking’”). p-hacking is manifest multiplicity; forking-paths is latent.

Signatures (each added when its primary is read)

  • undeclared-family — a test reported with no pre-specified analysis family (which comparisons / subgroups were candidates?). [mold scenario: forking-paths-post-hoc-interaction]
  • post-hoc subgroup / interaction chosen after seeing which cut “worked.”
  • data-contingent covariate / model / transformation / exclusion choices.
  • outcome or coding switched to the version that reached significance.

Primary sources to summarize (→ content/research/papers/<id>/)

Sourcing pre-verified in content/research/05-skill-backing-references.md §10 ([VS]). ✓ ingested = note exists at content/research/papers/<slug>/ and wiki-links back here.

  • Gelman & Loken 2014, American Scientist 102(6):460 (“The Statistical Crisis in Science”) + the 2013 Columbia working paper (“The garden of forking paths: Why multiple comparisons can be a problem, even when there is no ‘fishing expedition’ or ‘p-hacking’ and the research hypothesis was posited ahead of time”). The definition + the latent-multiplicity mechanism (procedure #3, T(y; φ(y))); offers no quantitative inflation figure (deferred to future work). → ✓ ingested gelman-loken-2014
  • Simmons, Nelson & Simonsohn 2011, Psychological Science 22(11):1359–1366 (DOI 10.1177/0956797611417632) — coins “researcher degrees of freedom”; quantifies the inflation Gelman-Loken leaves open (single flexibilities ≈9.5–12.6% at p<.05; all four combined 60.7%); the six-requirement disclosure remedy. → ✓ ingested simmons-2011

Remedy (what the referee demands)

  • Pre-specification / pre-registration: declare the analysis family (comparisons, subgroups, covariates, exclusions, primary outcome) before seeing the data.
  • Report across forks: multiverse analysis / specification-curve (content/research/04: Steegen & Gelman 2016 multiverse; Simonsohn 2020 specr).
  • Disclosure requirements — Simmons et al.’s six author requirements (Table 2: pre-set + reported stopping rule; ≥20/cell; list all variables; report all conditions incl. failed; report results with excluded obs included; report results without the covariate). (The compact “21-word solution” sentence is later work, not in Simmons 2011 — see simmons-2011 §11.) For subgroups: interaction tests + credibility criteria (Sun et al. 2010, content/research/05 §10 → subgroup-analysis skill).