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Тоҷикӣ
КыргызчаAI Copilot for Quantitative Research & Experimentation
From hypotheses to results: AI copilots that design studies, choose tests, analyze results, and ensure governance and reproducibility.
Experimentation velocity is limited by manual design, incorrect tests, and weak reproducibility. AI copilots can generate hypotheses, pick tests, simulate power, monitor experiments, and summarize results with caveats. Guardrails, datasets, and policy-as-code keep outputs compliant and trustworthy.
Test selection (t-test, MWU, chi-square, CUPED, sequential).
Assumption checks (normality, variance, independence, parallel trends).
Diagnostics for peeking, novelty, seasonality; counterfactuals.
Policy-as-code for metrics, eligibility, and test selection rules.
Prompt/tool layer with stat toolkit; calculators; templates for reports.
Governance: versioned experiments, approvals, lineage, audit trails.
Frequently asked questions
How do I create copilot prompts and launch design assistants?
Create copilot prompts/tools; launch design assistant; add report templates.
Can I enable runtime monitoring (SRM, peeking) to track experiments in real-time?
Enable runtime monitoring (SRM, peeking); add subgroup/slice analysis.
How can I implement governance controls such as approvals and lineage tracking for my experiments?
Roll out governance: approvals, lineage, reproducibility checks.
Can the AI copilot be scaled to handle quasi-experiments like DiD or synthetic control methods?
Scale to quasi-experiments (DiD, synthetic control) and multi-metric decisions.
▶ Try it live
Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.