Results for “experimental-data”

Design, prioritize, and evaluate product experiments with clear hypotheses and defensible decisions, including A/B testing, sample size estimation, and statistical interpretation.

将一篇经管实证论文摄取为实证研究 wiki：论文卡片 + 变量 + 数据 + 模型 + 机制 + 识别 + 稳健性 + 异质性 + 表格线索

基于已摄取文献和本地数据，生成经管实证研究设计：问题、机制、变量、模型、识别、机制、异质性、稳健性

Design product experiments around hypotheses, audience, metrics, guardrails, and decisions.

Analyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results", "compare", or needs to interpret experimental data.

Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads

Transforms A/B test and product experiment data into actionable readouts with hypothesis, metrics, interpretation, and decision.

Big Data Testing Expert Skill

Big Data Based Testing Skill

Build reproducible ML experiment plans with hypotheses, metrics, and ablations. Use when: (1) planning experiments, (2) comparing variants, (3) defining acceptance thresholds. NOT for: long-running experiment execution.

Pattern for separating repository code from user data and notebooks. Trigger: organizing scientific pipelines, separating code from data

Temporal Data

Use to build Science's data, code, and materials availability — mandatory deposition in approved repositories, accession numbers, a compliant data-availability statement, and materials/reagent sharing.

查看所有运行中实验的状态，可选自动收集已完成实验并推进流水线

Applied Big Data Testing Skill

Query Google Data Commons for public statistical data aggregated from global sources, resolving geographic entities and pulling time-series statistics. Use when working with demographic data, economic indicators, health statistics, or environmental data — population counts, GDP figures, unemployment rates, disease prevalence — or when resolving places to DCIDs and exploring relationships between statistical entities. Part of the AlterLab Academic Skills suite.

Derive selection thresholds from market data instead of hardcoding. Trigger when: (1) reviewing hardcoded parameters, (2) volume/price thresholds seem arbitrary, (3) selection returns too many/few candidates.

Mixup: Beyond Empirical Risk Minimization

基于证据优先的仓库执行工作流，适用于ECC。当用户需要运行命令、检查仓库、调试CI失败或推送带有精确执行和验证证明的窄修复时使用。