Statistics for Traders #41: The Clopper-Pearson exact binomial confidence interval — the two-sided generalization of yesterday's Rule of Three. Applied to today's slot 1 CHFJPY 4-of-4 DOWN at 1h: 95% two-sided CI on the true down-rate is [39.76%, 100%]. Coin-flip (50%) lies INSIDE the interval — 4/4 unanimity is NOT enough at 95% to reject a fair-coin null on n=4. It would take n=6 unanimity for Clopper-Pearson two-sided to clear the 50% threshold.
Yesterday’s Rule of Three gives a one-sided 95% upper CI ≈ 3/n on the true rate when we observe 0 successes in n trials. Today’s Clopper-Pearson (1934) generalizes to any k and gives the exact two-sided interval. Applied to today’s slot 1 CHFJPY 4/4 DOWN at 1h: 95% two-sided CI on the true down-rate is [39.76%, 100%]. Coin-flip (50%) lies inside — even 4/4 unanimity is not enough at 95% two-sided on n=4.
The exact formula
For n Bernoulli trials with k observed successes and confidence level 1-α, the Clopper-Pearson interval isp_L = BetaInv(α/2 ; k, n-k+1), p_U = BetaInv(1-α/2 ; k+1, n-k).
Equivalently, p_L is the smallest p such that P(X ≥ k | n, p) ≤ α/2, and p_U is the largest p such that P(X ≤ k | n, p) ≤ α/2 — the interval you get by inverting the cumulative binomial distribution's two tails. Coverage is provably ≥ 1-α (never undercovers), at the cost of being wider than the approximate intervals like Wilson score or Agresti-Coull.
Unanimity closed forms
The k=0 and k=n edge cases admit direct closed forms — no Beta-quantile solver needed. For k=n (all successes) at 95% two-sided (α = 0.05):p_L = (α/2)^(1/n) = 0.025^(1/n), p_U = 1.
Symmetric for k=0: p_L = 0, p_U = 1 - 0.025^(1/n). Yesterday’s Rule of Three drops the /2 factor for the ONE-SIDED variant: p_U = 1 - α^(1/n) = 1 - 0.05^(1/n) ≈ 3/n. The factor-of-2 difference between one-sided and two-sided translates to (0.025)^(1/n) vs (0.05)^(1/n) — a modest tightening for small n and negligible for large n.
Worked example — today’s CHFJPY 4/4 sample
| Method | 95% two-sided CI on down-rate | Rejects fair-coin? | Reading |
|---|---|---|---|
| Rule of Three complement (yesterday) | [25.00%, 100.00%] | no | widest — back-of-envelope |
| Clopper-Pearson exact (today) | [39.76%, 100.00%] | no | provably ≥95% coverage; DEFAULT |
| Agresti-Coull (adjusted Wald) | [45.41%, 100.00%] | no | Wald + 2-successes correction |
| Wilson score (Wilson 1927 — PT #41) | [51.01%, 100.00%] | YES | tightest; JUST clears 50% |
Wilson score is the only one whose 95% lower bound clears 50% — so ONLY Wilson can reject a fair-coin null on 4/4 unanimity at n=4. Clopper-Pearson’s conservative coverage keeps coin-flip inside the CI. That’s the correct honest reading: the CHFJPY safe-haven flip on Japan Prelim GDP big misses is very likely a real effect, but 4/4 unanimity on n=4 is at the edge of what CP can nail down at 95%.
![Two horizontal-panel visualization. Left panel: 95% CIs on the true down-rate from CHFJPY 4/4 DOWN at 1h sample (n=4, k=4). Point estimate 100%; Rule of Three complement [25%, 100%]; Clopper-Pearson [39.76%, 100%]; Agresti-Coull [45.41%, 100%]; Wilson score [51.01%, 100%]. Vertical coin-flip line at 50% shows only Wilson clears it. Right panel: same four methods on GBPJPY 0/4 UP at 15m from yesterday's slot 1 sample. Point estimate 0%; Rule of Three [0, 75%]; CP [0, 60.24%]; Agresti-Coull [0, 54.60%]; Wilson [0, 48.99%]. Wilson's upper 48.99% is the only one below 50%.](/insights/stats-for-traders-clopper-pearson/intervals.png)
What sample size would let Clopper-Pearson reject fair-coin?
| n unanimity | CP 95% two-sided lower | CP one-sided 95% lower | Clears 50%? |
|---|---|---|---|
| 3 | 0.2924 | 0.3684 | no / no |
| 4 | 0.3976 | 0.4729 | no / no (today's CHFJPY) |
| 5 | 0.4782 | 0.5493 | no / YES |
| 6 | 0.5423 | 0.6070 | YES / YES |
| 8 | 0.6389 | 0.6879 | YES / YES |
| 10 | 0.6915 | 0.7411 | YES / YES |
| 15 | 0.7834 | 0.8189 | YES / YES |
| 20 | 0.8341 | 0.8609 | YES / YES |
n=5 one-sided 95%: 0.05^(1/5) = 0.5493 → clears 50% one-sided. n=6 two-sided 95%: 0.025^(1/6) = 0.5423 → clears 50% two-sided. Applied to Vantage tail buckets: a big_miss bucket needs at least 5 consecutive same-direction prints to reject fair-coin one-sided by Clopper-Pearson at 95%, or 6 for two-sided. The JPY Prelim GDP × CHFJPY sample has 4 big_miss prints — one short of the one-sided threshold. A future recessionary Japan GDP print, if it followed the same safe-haven pattern, would push the sample to 5/5 and cross the one-sided rejection line (assuming CHFJPY continued the pattern).
Which method should you actually use?
Clopper-Pearson: default when guaranteed coverage matters more than tightness. This is Vantage’s default for slot-1 tail-unanimity caveat notes — its conservatism means we never overstate the reject-power of a small-sample unanimity. Wilson score: default when tightness matters more than worst-case coverage. Best average coverage across small n. Today’s PT #41 Wilson 1927 is the primary source — Wilson introduced the score interval 7 years before Clopper-Pearson introduced the exact interval, and also derived the k=0 special case that Hanley 1983 later named the Rule of Three. Agresti-Coull: pedagogically easier version of Wilson (Wald plus a 2-successes correction). Rule of Three: back-of-envelope for k=0 only. Never plain Wald: degenerates to [0,0] on k=0 and to [1,1] on k=n — useless for exactly the unanimity samples we care about.
Cross-links
Stats #40 (Rule of Three, 2026-09-09) — direct one-sided k=0 predecessor. Stats #12 (Bootstrap CI, 2026-08-12) — general CI method also handling the k=0 corner case via percentile resampling. Stats #7 (t-CI for the mean, 2026-08-07) — parametric t-interval that Clopper-Pearson generalizes for binomial proportions. Today’s slot 1 (CHFJPY 4/4 DOWN at 1h) — the direct 4-of-4 sample used as the worked example.