# confidence interval Published articles for confidence interval. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## One AI Output Is an Example, Not an Evaluation DevFeed: [One AI Output Is an Example, Not an Evaluation]() Original publisher: [Read original article]() Author: Raluca Budiu Published: 2026-08-14T17:00:00Z Content type: article Language: en Sources: [NN/g latest articles and announcements]() Topics: [Artificial Intelligence](), [Human-AI evaluation](), [LLM evaluation / benchmarking](), [benchmarking](), [User experience (UX)]() Tags: [ai](), [article](), [benchmarking](), [confidence-interval](), [eval](), [evaluation](), [language-models](), [llm](), [metrics](), [nondeterminism](), [performance](), [research](), [statistical-significance](), [usability]() ### AI overview One AI output is only an example, not a reliable evaluation. Because AI systems can produce different results from the same input, teams should assess them with multiple representative inputs, repeated runs, quantitative metrics, and confidence intervals. ### Source excerpt One output cannot establish how well an AI system performs. Evaluate with multiple representative inputs, repeated runs, and confidence intervals. ## Bootstrap Confidence Intervals for LLM Evaluation DevFeed: [Bootstrap Confidence Intervals for LLM Evaluation]() Original publisher: [Read original article]() Author: James Fiedler Published: 2026-07-08T14:30:02Z Content type: article Language: en Sources: [Indeed]() Topics: [LLM evaluation / benchmarking](), [Large Language Model]() Tags: [analytics](), [batching](), [confidence-interval](), [data-science](), [developers](), [evaluation](), [llm](), [llm-evaluation](), [machine-learning](), [metric](), [performance](), [research](), [variance](), [vs]() ### AI overview This article explains how to construct confidence intervals for non-deterministic LLM evaluations using bootstrap resampling. It recommends resampling inputs while carrying along all model runs for each selected input, then using the 2.5th and 97.5th percentiles for a 95% interval. It also discusses the tradeoff between the number of inputs and repeated runs, recommending k = 3 or 5 when feasible. ### Source excerpt Introduction As Large Language Models (LLMs) move from research prototypes to production systems, the developers of these systems need rigorous performance evaluation. In particular, we need confidence intervals around estimates of system accuracy. However, LLMs introduce a challenge that is unusual for ML systems: they are (operationally) non-deterministic. Even with the temperature set to zero, [...] ## Likelihood-ratio inference on differences in quantiles DevFeed: [Likelihood-ratio inference on differences in quantiles]() Original publisher: [Read original article]() Author: Miller, Evan Published: 2024-08-05T10:15:00Z Content type: article Language: en Sources: [Evan Miller]() Topics: [Statistics](), [Inference]() Tags: [confidence-interval](), [experimentation](), [statistics]() ### AI overview This paper presents a two-sample hypothesis test and confidence interval for differences in quantiles using a likelihood-ratio test statistic. A conservative version avoids density estimation, while another version uses a density estimator and produces confidence intervals close to nominal coverage. The method can be computed from four order statistics from each sample. ### Source excerpt Quantiles can represent key operational and business metrics, but the computational challenges associated with inference has hampered their adoption in online experimentation. In a new paper, I present a two-sample difference-in-quantile hypothesis test and confidence interval based on a likelihood-ratio test statistic. It can be computed using only four order statistics from each sample. arXiv link: Likelihood-ratio inference on differences in quantiles ## Monte Carlo Simulations: Separating Signal from Noise in Sampled Success Metrics DevFeed: [Monte Carlo Simulations: Separating Signal from Noise in Sampled Success Metrics]() Original publisher: [Read original article]() Author: Tom Lowe Published: 2023-01-03T12:20:27Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Simulation](), [data](), [pandas](), [Python](), [Shopify]() Tags: [confidence-interval](), [cost](), [data](), [guide](), [python](), [random](), [shopify](), [simulation]() ### AI overview This guide explains how Monte Carlo simulations can separate meaningful trends from sampling noise when success metrics cannot be measured across an entire population. It discusses sample size, confidence intervals, metric variability, and the tradeoff between statistical certainty and the cost of collecting more data. The article demonstrates the approach with the Shopify App Store and describes implementation in Python and pandas. ### Source excerpt Check out this guide for using a Monte Carlo simulation to identify the size and confidence percentage of your sampled success metric.