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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsA 1% sample is not automatically too small—or large enough. The bug is treating “1%” as the reason your AI reviewer is trustworthy. Without an absolute sample count, a defined question, human calibration, and an uncertainty estimate, the percentage says little about whether the result represents your evaluation set.
First decide what claim the evaluation needs to support
“Does the judge work?” is too broad to size a review sample. Decide what you want to estimate or decide; different questions can require different sampling designs.
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- Average quality: Estimate the judge’s error or agreement across the population of cases you care about.
- A model comparison: Determine whether the judge can reliably distinguish the systems on the metric and cases relevant to that comparison.
- A regression alert: Assess whether the judge can detect the change you care about, with uncertainty appropriate to the cost of a false alert or a missed regression.
- A rare-failure rate: Make sure the design can observe and estimate infrequent but consequential failures. A small random sample may contain too few such cases to support a useful conclusion.
For each question, specify the target population, the estimate or decision, and the uncertainty or statistical power you need. Then plan the sample. A percentage alone supplies none of those justifications, and the reviewed studies do not establish a universally optimal review percentage or sample count.
Use human ratings to calibrate the judge
A practical mixed design is to run the LLM judge on all observations when feasible, collect human ratings on a planned subset, and combine both sources with an estimator designed for that setup. The 2026 paper “Augmenting Human Evaluation with LLM Judges: How Many Human Reviews Do You Need?” describes this as a two-stage sampling design and uses a doubly robust estimator. Its authors propose planning the human and LLM rating sample sizes around target power, rather than choosing a fixed fraction by convention.
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This approach makes human labels calibration evidence, not just a token spot check. It is a design proposal, not a guarantee that every project should judge every case or use one estimator. Applying it still requires choosing a suitable estimand, sampling procedure, and inferential method for the evaluation at hand.
Make the human subset match the sampling design
Random selection can support inferences about the corresponding population under the assumptions of the chosen method. But random sampling may leave rare or high-risk categories underrepresented. If you deliberately oversample edge cases or draw a stratified sample, record that design and use an estimator that accounts for it; do not analyze the labels as though they were a simple random sample.
The 2026 evalstats preprint discusses mixed human-AI inference under a missing-completely-at-random assumption for its random-subset case. Its methods should not be treated as automatically covering every non-random sampling plan. See “How to Run Statistics over LLM Judges and Trust the Results: Calibrated Inference for Small-Sample AI Evaluation with evalstats” for the assumptions and methods described by its authors.
Check both agreement with people and stability
A judge can give repeatable scores yet disagree with human raters. It can also align with people on one prompt and change its judgments when the prompt wording changes. These are distinct reliability questions: human alignment concerns whether the judge’s ratings track human assessments, while prompt stability concerns whether judgments remain consistent under prompt variation.
The evalstats preprint’s authors give ρ² ≥ 0.4 as a rough point where meaningful gains from mixed judge-human designs begin, and advise that ρ² < 0.2 is too poor for the judge to use. Treat these as that paper’s guidance, not universal pass/fail thresholds. Whether a judge is useful depends on the target decision and the consequences of error. The ICML 2026 paper “How to Correctly Report LLM-as-a-Judge Evaluations” and ICML 2026 paper “Diagnosing the Reliability of LLM-as-a-Judge via Item Response Theory” address statistical reporting and reliability dimensions, respectively.
What to report so others can judge the result
A headline agreement score or sample percentage is not enough to audit a judge-based evaluation. Report the details that let a reader assess what was measured, how the human comparison was built, and how much uncertainty remains.
- Target claim: State the population, estimand, comparison, or decision the evaluation is intended to support.
- Judge specification: Name the exact model and provide the prompt and relevant configuration.
- Sampling: Give the number of cases reviewed by humans, the sampling frame and selection method, and any strata or deliberate oversampling.
- Human-rating process: Describe who rated the cases and how human ratings were obtained and handled.
- Validation: Report the judge-human alignment measure and how judge stability under prompt changes was assessed.
- Inference: Name the estimator or test, state the assumptions it relies on, and report uncertainty and effective sample size where applicable.
These details make it possible to distinguish a calibrated estimate from a convenient but unsupported extrapolation. The evalstats preprint also provides reporting guidance; its assumptions should be reported rather than left implicit.
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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Choose the sample for the inference, not the round number
For a budget-limited evaluation, decide what conclusion matters, choose a sampling design that represents the relevant cases, and use human labels to test how the judge performs on that design. Increase or redesign the human sample until it supports the specified inference; if the available budget cannot do that, describe the result as exploratory rather than presenting an arbitrary fraction as validation. There is no evidence here for a blanket ranking of 1% against 5%: the useful sample depends on the question, case mix, judge-human alignment, and tolerated uncertainty.
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