allowed-tools: Task, Read, Write, Bash, Grep, Glob

argument-hint: [dataset] [domain]

description: Generate research hypotheses and experimental designs based on data patterns

Hypothesis Generation Command

Generate research hypotheses and experimental designs for dataset $1 in domain $2 using the hypothesis-generator subagent.

Context

• Dataset location: @data_storage/$1
• Analysis domain: $2 (user-behavior, business-impact, technical-performance, custom)
• Current working directory: !pwd
• Output directory: ./hypothesis_reports/
• Available research methodologies and experimental designs
• Statistical analysis capabilities

Your Task

Use the hypothesis-generator subagent to create rigorous, testable hypotheses:

1. Pattern Analysis

• Identify significant correlations and relationships
• Detect temporal patterns and trends
• Discover clusters and segments in the data
• Recognize anomalies and unusual patterns

2. Hypothesis Formulation

• Create clear, testable hypotheses
• Define null and alternative hypotheses
• Specify variables and their relationships
• Establish measurable outcomes and success criteria

3. Experimental Design

• Select appropriate research methodologies
• Design experimental approaches to test hypotheses
• Determine sample size and power requirements
• Plan data collection and measurement procedures

4. Validation Strategy

• Define statistical testing approaches
• Establish success criteria and metrics
• Plan for replication and verification
• Consider alternative explanations and approaches

Analysis Domains

User Behavior

• Engagement Patterns: User interaction and engagement hypotheses
• Conversion Optimization: Conversion rate and funnel analysis hypotheses
• Retention and Churn: User retention and churn prediction hypotheses
• Segmentation: User behavior segmentation hypotheses
• Journey Analysis: User journey and path analysis hypotheses

Business Impact

• Revenue Optimization: Revenue generation and growth hypotheses
• Cost Reduction: Cost efficiency and optimization hypotheses
• Market Expansion: Market growth and expansion hypotheses
• Customer Satisfaction: Customer experience and satisfaction hypotheses
• Operational Efficiency: Process improvement and efficiency hypotheses

Technical Performance

• System Optimization: Performance and scalability hypotheses
• Reliability: System stability and reliability hypotheses
• Security: Security vulnerability and protection hypotheses
• User Experience: Technical UX and performance hypotheses
• Integration: System integration and compatibility hypotheses

Custom

• Domain-Specific: Custom domain-specific hypotheses
• Research-Oriented: Academic and research hypotheses
• Experimental: Novel experimental hypotheses
• Predictive: Predictive modeling hypotheses

Hypothesis Types

Descriptive Hypotheses

Describe patterns and relationships in the data without inferring causation.

Example: "There is a positive correlation between user engagement time and conversion rates."

Explanatory Hypotheses

Explain underlying mechanisms and causal relationships.

Example: "Increased user engagement time leads to higher conversion rates due to improved product understanding."

Predictive Hypotheses

Forecast future outcomes based on current patterns.

Example: "Users with engagement time > 5 minutes are 3x more likely to convert within 30 days."

Prescriptive Hypotheses

Recommend optimal actions and interventions.

Example: "Implementing personalized recommendations will increase user engagement by 25%."

Research Methodologies

Experimental Designs

• A/B Testing: Randomized controlled experiments
• Multivariate Testing: Multiple variable experiments
• Longitudinal Studies: Time-series analysis
• Cross-sectional Studies: Point-in-time analysis
• Quasi-experiments: Non-randomized designs
• Case Studies: In-depth analysis of specific cases

Statistical Approaches

• Hypothesis Testing: Statistical significance testing
• Confidence Intervals: Estimation of effect sizes
• Bayesian Methods: Bayesian hypothesis testing
• Power Analysis: Statistical power calculation
• Effect Size Measurement: Quantifying relationship strength

Validation Methods

• Cross-validation: Model validation techniques
• Bootstrapping: Resampling validation
• Sensitivity Analysis: Testing robustness
• Replication Studies: Independent verification
• Meta-analysis: Synthesis of multiple studies

Expected Output

Hypothesis Documentation

• hypothesis_reports/$1_$2_hypotheses.md - Hypothesis documentation
• hypothesis_reports/$1_$2_experimental_design.md - Experimental design
• hypothesis_reports/$1_$2_validation_plan.md - Validation strategy
• hypothesis_reports/$1_$2_research_proposal.md - Research proposal

Hypothesis Structur