「hypothesis」の検索結果

Search the FPF knowledge base and display hypothesis details with assurance information

日本語の概要は準備中です。原文の説明を表示しています。

Root-cause a failing or wrong empirical result with a disciplined reproduce → minimise → hypothesise → instrument → fix loop, instead of guessing-and-poking. Use when the user says "why is my regression wrong", "this number changed", "my script errors out", "the result won't reproduce", "debug this", "this estimate looks wrong", or "it worked yesterday". Tuned for research code (R/Stata/Python): type coercion, NA/merge blow-ups, factor levels, clustering/SE choices, weighting, collinearity/convergence, seeds, package-version drift. Use `--no-fix` to localize the root cause without editing shared or load-bearing files.

日本語の概要は準備中です。原文の説明を表示しています。

Applies the scientific method to debugging by helping users form specific, testable hypotheses, design targeted experiments, and systematically confirm or reject theories to find root causes. Use when a user says their code isn't working, they're getting an error, something broke, they want to troubleshoot a bug, or they're trying to figure out what's causing an issue. Concrete actions include isolating failing components, forming and testing hypotheses, analyzing error messages, tracing execution paths, and interpreting test results to narrow down root causes.

日本語の概要は準備中です。原文の説明を表示しています。

Performs systematic root cause analysis to identify the true source of bugs, errors, and unexpected behavior through structured investigation phases — not just treating symptoms. Use when a user reports a bug, crash, error, or broken behavior and needs to debug, troubleshoot, or investigate why something is not working; especially for complex or intermittent issues across multiple components. Applies the Five Whys method, hypothesis-driven testing, stack trace analysis, git blame/log evidence gathering, and causal chain documentation to isolate and confirm root causes before applying any fix.

日本語の概要は準備中です。原文の説明を表示しています。

Experiment design reference for problem-class framing, Minimum Viable Experiment coaching, hypothesis formation, vetting and red flags, and experiment readiness. Use when translating a stated business outcome into candidate data-science problem classes, or when framing, vetting, scoping, or evaluating an experiment of any kind, including data feasibility, architecture, LLM, performance, use-case, UX, prototyping, and hardware experiments.

日本語の概要は準備中です。原文の説明を表示しています。

Build a tiered classroom behavior intervention plan (BIP) for a K-12 student, grounded in the function of the behavior. Use when asked to plan a behavior intervention, address a disruptive or off-task pattern, write a BIP, or set up positive behavior supports. Produces a function hypothesis, prevention/antecedent strategies, teaching of a replacement behavior, a response plan for when it happens, and a simple data-tracking method — positive and skill-building, not punitive.

日本語の概要は準備中です。原文の説明を表示しています。

Design statistically rigorous A/B tests for product features, UI changes, onboarding flows, and pricing experiments. Use when asked to set up an experiment, design an A/B test, calculate sample size, or interpret test results. Produces a complete test plan with hypothesis, variant definitions, sample size, duration estimate, guardrail metrics, and a results interpretation guide.

日本語の概要は準備中です。原文の説明を表示しています。

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

日本語の概要は準備中です。原文の説明を表示しています。

Produce an experiment blueprint from a research hypothesis

日本語の概要は準備中です。原文の説明を表示しています。

Run fast, progressive experiments to map tunable parameters and their effects. Use when optimizing code, prompts, configurations, or other artifacts against a goal or benchmark, or when a long research run needs a planned hypothesis queue, focused trials, and durable findings.

日本語の概要は準備中です。原文の説明を表示しています。

Rules as hypotheses: falsifiable tests, confidence updates, graduate or kill. Triggers: experiment, hypothesis, prove, test rule, validate methodology, evidence.

日本語の概要は準備中です。原文の説明を表示しています。

Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.

日本語の概要は準備中です。原文の説明を表示しています。

Apply step-by-step analysis for complex problems with revision capability. Use for multi-step reasoning, hypothesis verification, adaptive planning, problem decomposition, course correction.

日本語の概要は準備中です。原文の説明を表示しています。

Assess a field lesson for reuse or product feedback without exposing customer context. Use for recurring deployment lessons; distinguish a hypothesis from a validated pattern.

日本語の概要は準備中です。原文の説明を表示しています。

How to read experiment results without fooling yourself. Confidence intervals, p-values, multiple testing, sequential testing, CUPED, heterogeneous treatment effects, ratio metrics, network effects, dashboard reconciliation, and the interpretation failures that produce confidently wrong shipping decisions. Use this skill whenever the user is reading a finished experiment result panel and about to make a ship, kill, or iterate decision, or when an experiment number does not match the dashboard number. Triggers on read experiment results, result panel, ship or kill decision, p-value, confidence interval, statistical significance, multiple testing, peeking, sequential testing, CUPED, variance reduction, heterogeneous treatment effects, ratio metric, network effects, inconclusive test, experiment versus dashboard mismatch. Use `experiment-design` instead when the test has not run yet and the question is hypothesis, sample size, duration, or what to test.

日本語の概要は準備中です。原文の説明を表示しています。

Hypothesis generation from scientific literature and data patterns

日本語の概要は準備中です。原文の説明を表示しています。

Systematic root-cause debugging. Iron Law: no fixes without root cause investigation first. Four phases: Root Cause Investigation, Pattern Analysis (6 known patterns), Hypothesis Testing (3-strike rule), Implementation. Structured debug report output.

日本語の概要は準備中です。原文の説明を表示しています。

Parses error messages, traces execution flow through stack traces, correlates log entries, and applies systematic hypothesis-driven methodology to isolate and resolve bugs.

日本語の概要は準備中です。原文の説明を表示しています。

Design and run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."

日本語の概要は準備中です。原文の説明を表示しています。

Data analysis across SQL, visualization, statistics, and reporting. Use when writing SQL queries, building dashboards, performing cohort or funnel analysis, running hypothesis tests, or presenting data-driven recommendations.

日本語の概要は準備中です。原文の説明を表示しています。

Applied statistics for business and product questions — test selection, assumption checks, power planning, effect sizes with intervals, multiplicity correction. Use when interpreting an experiment, sizing a study, or vetting a claim.

日本語の概要は準備中です。原文の説明を表示しています。

Chaos engineering: hypothesis-driven fault injection to surface weakness before users do. Use when designing a chaos experiment, planning a gameday, choosing what to inject, computing blast radius, or building a chaos maturity model.

日本語の概要は準備中です。原文の説明を表示しています。

When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see analytics-tracking.

日本語の概要は準備中です。原文の説明を表示しています。

Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.

日本語の概要は準備中です。原文の説明を表示しています。