Statistics for Traders #42: The Wilson score confidence interval — invert the score test rather than the Wald test, and the resulting closed form (Wilson 1927 Section 2) is TIGHTER than yesterday's Clopper-Pearson exact interval on every unanimity sample the last three days have generated. Applied to today's slot 1 NZDCAD small_miss 2 of 11 UP: Wilson 95% CI on up-rate = [5.1%, 47.7%] cleanly rejects fair-coin where Clopper-Pearson's [2.4%, 51.8%] does not.
For n Bernoulli trials with k successes, the Wilson score interval is (p̂ + z²/(2n) ± z · √(p̂q̂/n + z²/(4n²))) / (1 + z²/n) — derived by inverting the score test rather than the Wald test. Wilson (1927), the paper yesterday’s PT #41 verified from primary source, worked this out 71 years before Agresti & Coull rediscovered the same shrinkage adjustment (see today’s PT #42). Applied to today’s slot 1 NZDCAD 2-of-11 UP sample: Wilson 95% CI on up-rate = [5.1%, 47.7%]— cleanly rejects fair-coin. Yesterday’s Clopper-Pearson gives [2.4%, 51.8%] — includes fair-coin, fails to reject.

Derivation (Wilson 1927 Section 2)
The score-test rejects H0: p = p0 when (p̂ - p0) / √(p0(1-p0)/n) exceeds zα/2 in absolute value. Setting z² · p0(1-p0)/n = (p̂ - p0)² and rearranging gives the quadratic in p0: p0² · (1 + z²/n) − p0 · (2p̂ + z²/n) + p̂² = 0. Its two roots are the Wilson lower and upper endpoints. Standard completing-the-square gives the closed form above. Wilson (1927 p. 3, verified verbatim from primary source in yesterday’s PT #41) wrote out this exact derivation 40 years before confidence intervals had a formal frequentist definition — Neyman 1935 later credited Wilson as the first frequentist CI paper.
Four methods on the past four days of unanimity samples
| Sample | RoT #40 | CP #41 | Agresti-Coull | Wilson #42 | Which reject 50%? |
|---|---|---|---|---|---|
| n=4, k=4 (CHFJPY 4/4 down, 09-10) | [25.0, 100] | [39.8, 100] | [45.4, 100] | [51.0, 100] | Wilson only |
| n=4, k=0 (GBPJPY 0/4 up, 09-09) | [0, 75.0] | [0, 60.2] | [0, 54.6] | [0, 49.0] | Wilson only |
| n=11, k=0 (NZDJPY 0/11, 09-10) | [0, 27.3] | [0, 28.5] | [0, 30.0] | [0, 25.9] | all four |
| n=11, k=2 (NZDCAD 2/11, today) | n/a (k>0) | [2.4, 51.8] | [4.0, 48.8] | [5.1, 47.7] | Wilson + AC |
Wilson is the TIGHTEST interval in every row. On the two n=4 unanimity samples, Wilson is the ONLY method that rejects fair-coin. On the n=11 k=2 sample (today), Wilson AND Agresti-Coull reject; Clopper-Pearson does not. Rule of Three only applies when k=0 (or k=n via complement); on n=11 k=2, RoT is not defined. The reading: as sample sizes grow, all four methods converge — at n=11 with tail-bucket unanimity (k=0), even yesterday’s Rule of Three is essentially as tight as Wilson. But on the borderline n=11 with k=2 today, only the two “interval” methods (Wilson and AC) survive the fair-coin test.
Wilson point estimator — the “+2 successes and +2 failures” shrinkage
The center of the Wilson CI is p̃ = (X + z²/2)/(n + z²). At z² ≈ 4 (95%), this simplifies to p̃ = (X + 2)/(n + 4)— the same “add two successes and two failures” adjustment Agresti & Coull 1998 (today’s PT #42) recommended for use in introductory statistics classes. Wilson himself proposed the same shrinkage estimator in the 1927 paper (verified verbatim in yesterday’s PT #41 primary source): “Those who make the usual allowance of 2σ for drawing an inference would use (S+2)/(n+4).” Applied to today’s NZDCAD 2/11 sample: p̃ = 4/15 = 0.267 — a Wilson-shrunk estimate 8.5pp higher than raw p̂ = 0.182 (the shrinkage pulls toward 0.5, the size shrinking as n grows).
Coverage caveat and when to prefer something else
Wilson has narrow “coverage spikes” near p=0 and p=1 where its actual coverage dips to ~83% at pathological p-values (Agresti-Coull 1998 Figure 4, verified from primary source in today’s PT #42). For unanimity samples on n < 5, the two-sided Wilson interval can undercover in this spike region. For n < 5, prefer the ONE-SIDED Rule of Three — at n=5 unanimity, the RoT one-sided 95% lower bound is 1 − 0.051/5= 54.9%, which cleanly rejects fair-coin without touching Wilson’s coverage-spike region. For n ≥ 5 with any k, Wilson is the recommended default: Agresti & Coull (1998) conclude explicitly that “the score confidence interval tends to perform much better than the exact or Wald intervals in terms of having coverage probabilities close to the nominal confidence level. It can be recommended for use with nearly all sample sizes and parameter values.”
Verification note
All CI numbers cross-checked against Python statsmodels.stats.proportion.proportion_confint(method='wilson') on 2026-09-11. The 3-day arc Stats #40 (Rule of Three) → #41 (Clopper-Pearson) → #42 (Wilson score) uses the same daily-routine unanimity samples as running examples, so the running comparison table doubles as the running index of the arc so far. Chart via one-off script (4-panel horizontal CI plot at (n=4, k=4), (n=4, k=0), (n=11, k=0), (n=11, k=2)) reusing embedded svg + sharp, not committed under scripts/. Next in the arc: Agresti-Coull adjusted-Wald derivation (Stats #43 queued), Jeffreys Beta(0.5, 0.5) prior interval (Bayesian sibling), Fisher exact test, Rule of Five for x=1.