blind-panel

Blind pairwise evaluation — seeded blinding, position-bias detection, and inter-rater agreement.

The win rate is the number everyone quotes and the number that means least.

npx blind-panel prepare --candidates=a,b --items=one,two,three --out=./run --seed=r1
# judges see run/manifest.json — never run/key.json
npx blind-panel tally --dir=./run --verdicts=./verdicts.json

Zero dependencies. MIT.

The reasoning behind it is published as a whitepaper — The Blind Panel: Position Bias and Agreement as Preconditions for a Win Rate.

Why

When you put candidates in front of a panel — human or model — and ask which is better, two numbers decide whether the answer means anything, and neither is usually reported:

Position bias. If judges chose "left" far from half the time, they were responding to position rather than content, and every win rate is void. Trivial to compute. Almost never computed.

Inter-rater agreement. Near chance means the panel detected no shared signal, so the win rate is noise however decisive it looks. High agreement means either a real difference or a bias the judges share — this statistic cannot separate those, and the tool says so rather than letting the ambiguity pass for rigour.

Either condition exits non-zero, because a run that failed them has not measured anything and should not be reported as though it had.

Confidence you can put in front of a client

Every win rate ships with a Wilson score interval and an exact binomial test, and a verdict that requires both.

The textbook p ± z·sqrt(p(1-p)/n) fails exactly where evaluation panels live — small n, lopsided results. At 0 or n wins it collapses to zero width, reporting "100%, ±0" from four comparisons. It emits bounds below 0 and above 1, which are not probabilities. Its real coverage below n≈40 is well under the nominal 95%, so it overstates confidence precisely where a reader needs the opposite.

Wilson is bounded in [0,1] by construction and behaves at the extremes. It is asymmetric, so both bounds are reported rather than a single ± that would discard the asymmetry.

Wilson is still an approximation, and at very small n it runs slightly liberal. A perfect 4-of-4 gives a Wilson lower bound of 0.5101 — excluding 0.5, so it would be called a win — while the exact two-sided p is 0.125. Four straight wins is what a fair coin does one time in eight. A directional verdict therefore requires the interval to exclude 0.5 AND the exact test to reach α.

wins/n	win rate	Wilson 95%	exact p	verdict
4/4	1.000	[0.510, 1.000]	0.125	inconclusive
8/11	0.727	[0.434, 0.902]	0.227	inconclusive
80/110	0.727	[0.637, 0.802]	0.000002	better
55/100	0.550	[0.452, 0.644]	0.368	inconclusive
0/20	0.000	[0.000, 0.161]	0.000002	worse

8/11 and 80/110 are the same 72.7%. Only one of them is a result.

What it does not do

It does not judge. It handles blinding, randomisation, unblinding and statistics; who or what looks at the candidates is yours. That separation is the point: anyone holding the seed can re-derive the assignment and check the arithmetic instead of trusting your summary.

Balanced by construction

Side assignment is not an independent coin flip per pair. With six items, pure random puts every one on the same side about 3% of the time — and when it does, the candidate's identity is perfectly confounded with position, which is exactly the failure this package exists to detect.

Instead each candidate gets an equal number of left and right placements, shuffled by a seeded Fisher-Yates. Balance is guaranteed; the individual assignment is still unpredictable.

(This was found by the first smoke run of this package producing candidateLeft: 6, candidateRight: 0. It is now a regression test.)

Usage

Explicit items

blind-panel prepare --candidates=modelA,modelB --items=q1,q2,q3 --out=./run --seed=run-1

Directory mode

Candidate names come from directory basenames; items are the files present in every candidate and the reference. Anything missing from a candidate is dropped loudly — comparing a candidate on an item another candidate lacks would weight the panel silently.

blind-panel prepare --candidates=./out/a,./out/b --reference=./out/ref --out=./run

Verdicts

[
  { "pairId": "modelA--q1", "judge": "alice", "choice": "left" },
  { "pairId": "modelA--q1", "judge": "bob",   "choice": "right" }
]

blind-panel tally --dir=./run --verdicts=./verdicts.json

Exit 0 means the run is usable. Exit 1 means it is not, and says why.

As a library

import { buildPairs, tally, problemsWith, sideBalance } from 'blind-panel';

Origin

Extracted from a project that needed to judge three candidate visual designs and wanted the judging to be worth something. The first smoke run of this package produced the six-zero result described above — the confound it exists to detect, occurring inside it — which is written up in §2 of the paper.

The methodology owes a debt to mshumer/Claude-of-Duty, which ran eleven critics in blind A/B against real reference frames and published the result honestly, but shipped no tooling for it. This is that procedure made executable and checkable.

More from iSimplifyMe

• The Blind Panel — the paper behind this package
• The Trust Ladder — how a non-deterministic gate earns authority on measured precision
• The Finite Chain — the same construction-over-monitoring argument, applied to agent autonomy
• The AEO Standard and aeo-scan — the same tool-plus-rubric-plus-paper shape, for answer-engine optimization
• All whitepapers

License

MIT © iSimplifyMe