scientific-validation

name	scientific-validation
description	Scientific method for validating claims with pre-registration, power analysis, statistical rigor, and Bayesian methods. Use when testing hypotheses, running experiments, or validating claims from papers. TRIGGER when: validate, hypothesis, experiment, backtest, evidence, statistical test. DO NOT TRIGGER when: routine coding, config changes, documentation, non-experimental tasks.
allowed-tools	["Read","Grep","Glob","Bash","Write"]

Scientific Validation Skill

Rigorous methodology for validating claims from any source - books, papers, theories, or intuition.

When This Skill Activates

• Testing claims from books, papers, or expert sources
• Validating rules, strategies, or hypotheses
• Running experiments or backtests
• Keywords: "validate", "test hypothesis", "experiment", "backtest", "prove", "evidence"

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Core Principle

Data is the arbiter. Sources can be wrong.

• Expert books can be wrong
• Only empirical validation decides what works
• Document negative results - they're valuable

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Phase Overview

Phase	Name	Key Requirement
0	Claim Verification	Understand what source ACTUALLY claims
1	Claims Extraction	Document with source citations
1.5	Publication Bias Prevention	Document ALL claims before selecting
2	Pre-Registration	Hypothesis BEFORE seeing results
2.3	Power Analysis	Calculate required n (MANDATORY)
3	Bias Prevention	Look-ahead, survivorship, selection
3.5	Walk-Forward	Required for time series (MANDATORY)
4	Statistical Requirements	p-values, effect sizes, corrections
4.7	Bayesian Complement	Bayes Factors for ambiguous results
5	Multi-Source Validation	Test across 3+ contexts
5.3	Sensitivity Analysis	±20% parameter stability (MANDATORY)
5.5	Adversarial Review	Invoke experiment-critic agent
6	Classification	VALIDATED / REJECTED / INSUFFICIENT
7	Documentation	Complete audit trail
7.3	Negative Results	Structured failure documentation

See: workflow.md for detailed step-by-step instructions per phase.

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Quick Reference

Claim Types

Type	Testable?	Example
PERFORMANCE	YES	"A beats B on metric X"
METHODOLOGICAL	YES	"A enables capability X"
PHILOSOPHICAL	MAYBE	"X is important because Y"
BEHAVIORAL	HARD	"Humans do X in situation Y"

Sample Size Requirements (80% Power)

Effect Size	Cohen's d	Required n
Small	0.2	394
Medium	0.5	64
Large	0.8	26

See: code-examples.md#power-analysis for calculation code.

Classification Criteria

Status	Criteria
VALIDATED	OOS meets all criteria + critic PROCEED
CONDITIONAL	OOS meets relaxed criteria (p < 0.10)
REJECTED	OOS fails OR negative effect
INSUFFICIENT	n < 15 in OOS
UNTESTABLE	Required data unavailable
INVALID	Circular validation detected

Domain Effect Thresholds (Trading)

Metric	Minimum	Strong	Exceptional
Sharpe Ratio	> 0.5	> 1.0	> 2.0
Win Rate	> 55%	> 60%	> 70%
Profit Factor	> 1.2	> 1.5	> 2.0

See: code-examples.md#effect-thresholds for other domains.

Bayes Factor Interpretation

BF	Evidence
< 1	Supports null
1-3	Anecdotal
3-10	Moderate
10-30	Strong
> 30	Very strong

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Critical Rules

1. Pre-Registration

• Document hypothesis BEFORE seeing any results
• Define success criteria BEFORE testing
• No peeking at test data

2. Power Analysis (Phase 2.3)

from statsmodels.stats.power import TTestIndPower
n = TTestIndPower().solve_power(effect_size=0.5, power=0.80, alpha=0.05)

Rule: Underpowered studies cannot achieve VALIDATED status.

3. Walk-Forward for Time Series (Phase 3.5)

• Standard K-fold CV → INVALID (temporal leakage)
• Single train/test → CONDITIONAL at best
• Walk-forward → Can achieve VALIDATED

See: code-examples.md#walk-forward for implementation.

4. Multiple Comparison Correction

alpha_corrected = 0.05 / num_claims  # Bonferroni

For trading claims: require t-ratio > 3.0 (Harvey et al. standard).

5. Sensitivity Analysis (Phase 5.3)

Test ±20% parameter variation:

• All variations positive → Can achieve VALIDATED
• 1-2 sign flips → CONDITIONAL at best
• 3+ sign flips → REJECTED (fragile)

See: code-examples.md#sensitivity-analysis for implementation.

6. Adversarial Review (Phase 5.5)

Use Task tool:
  subagent_type: "experiment-critic"
  prompt: "Review experiment EXP-XXX"

MANDATORY before any classification.

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Bias Prevention Checklist

Bias	Prevention
Look-ahead	Process data sequentially, compare batch vs streaming
Survivorship	Track ALL attempts, not just completions
Selection	Report ALL experiments including failures
Data snooping	Strict train/test split, no tuning on test data
Publication	Document ALL claims before selecting which to test

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Pre-Experiment Checklist

• Claim extracted with source citation
• ALL claims documented (not just tested ones)
• Hypothesis documented BEFORE results
• Power analysis: required n calculated
• Success criteria defined
• Walk-forward configured (time series)
• Costs/constraints specified

Post-Experiment Checklist

• Sample size adequate per power analysis
• p-value AND effect size reported
• Bayesian analysis if ambiguous
• Sensitivity analysis passed
• Adversarial review completed
• Negative results documented if REJECTED

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Red Flags

• 100% success rate → Possible bias
• OOS better than training → Possible leakage
• Result flips with ±20% params → Fragile
• Only tested "interesting" claims → Selection bias

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Key Principles

1. Hypothesis BEFORE data - No peeking
2. Power analysis BEFORE experiment - Know required n
3. Walk-forward for time series - Preserve temporal order
4. Sensitivity analysis - Results must survive ±20% changes
5. Adversarial self-critique - Challenge your methodology
6. Document negative results - Failures are valuable
7. Sources can be wrong - Even experts, even textbooks

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Detailed Documentation

Topic	File
Step-by-step workflow	workflow.md
Python code examples	code-examples.md
Markdown templates	templates.md
Adversarial review	../../agents/experiment-critic.md

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Hard Rules

FORBIDDEN:

• Reporting results without confidence intervals or statistical significance
• Cherry-picking favorable metrics while ignoring unfavorable ones
• Claiming causation from correlation without controlled experiments

REQUIRED:

• All experiments MUST have a documented hypothesis before execution
• All results MUST include sample size, variance, and statistical test used
• Negative results MUST be reported with the same rigor as positive results
• Baselines MUST be established and compared against for every metric