Overview

Probably is built on scientific principles to ensure that data analysis produces reliable, reproducible, and trustworthy insights that stand up to scrutiny.

Probably implements fundamental scientific principles in every analysis:

Hypothesis-Driven Analysis

• Clear Questions: Start with specific, testable questions
• Falsifiable Hypotheses: Formulate hypotheses that can be proven wrong
• Evidence-Based Conclusions: Draw conclusions only from data evidence
• Null Hypothesis Testing: Test against the null hypothesis to avoid bias

Reproducibility and Transparency

• Documented Methodology: Every analysis step is recorded and explainable
• Reproducible Results: Same data and methods produce same results
• Open Methodology: Analysis methods are transparent and auditable
• Version Control: Track changes and iterations in analysis

Systematic Approach

• Structured Process: Follow consistent methodology for all analyses
• Control Variables: Account for confounding factors
• Sample Integrity: Ensure data quality and representativeness
• Statistical Validity: Apply appropriate statistical methods

Structured Analysis Approach

• Clear Questions: AI agent guides systematic question formulation
• Visual Analysis: Consistent approach to creating analytical visualizations
• Context Awareness: Identification of relevant variables and relationships
• Transparent Process: Clear explanations of analytical choices

Analysis Documentation

• Query History: Record of all questions asked and charts generated
• Methodology Transparency: Explanation of chart types and variable selections
• Decision Rationale: Clear reasoning behind analytical approaches
• Result Context: Contextual interpretation of findings

Data Quality Validation

┌─────────────────────────────────────────────────────────────────────────────┐
│                          Data Quality Framework                             │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌─────────────────┐    ┌─────────────────┐    ┌─────────────────────────┐  │
│  │ Data Integrity  │    │ Statistical     │    │    Methodology         │  │
│  │ Validation      │    │ Validity        │    │    Validation           │  │
│  │                 │    │                 │    │                         │  │
│  │ • Completeness  │    │ • Significance  │    │ • Approach Selection    │  │
│  │ • Accuracy      │    │ • Effect Size   │    │ • Assumption Checking   │  │
│  │ • Consistency   │    │ • Confidence    │    │ • Bias Detection        │  │
│  │ • Timeliness    │    │ • Power Analysis│    │ • Validity Assessment   │  │
│  └─────────────────┘    └─────────────────┘    └─────────────────────────┘  │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Bias Detection and Mitigation

• Selection Bias: Identify and correct for sample selection issues
• Confirmation Bias: Challenge assumptions and test alternative hypotheses
• Survivorship Bias: Account for missing or excluded data
• Temporal Bias: Consider time-dependent effects and seasonality

Types of Analysis Designs

• Observational Studies: Analyze existing data without intervention
• Natural Experiments: Leverage naturally occurring variations
• A/B Testing: Controlled comparison of different approaches
• Time Series Analysis: Longitudinal studies of changes over time

Control and Causation

• Confounding Variables: Identify and control for confounders
• Randomization: Use random sampling where possible
• Stratification: Control for known variables through stratification
• Instrumental Variables: Use instrumental variables for causal inference

Systematic Visualization

• Appropriate Chart Selection: Choose charts based on data types and relationships
• Comparative Analysis: Generate multiple views to understand patterns
• Pattern Recognition: Identify trends and relationships through visual exploration
• Context Integration: Include relevant variables for comprehensive understanding

Quality Practices

• Data Validation: Check for missing values and data quality issues
• Multiple Perspectives: Examine data from different analytical angles
• Consistent Methodology: Apply systematic approach to chart generation
• Transparent Interpretation: Provide clear explanations of findings

Evidence Hierarchy

1. Systematic Reviews: Meta-analysis of multiple studies
2. Randomized Controlled Trials: Gold standard for causal inference
3. Cohort Studies: Longitudinal observational studies
4. Case-Control Studies: Retrospective comparison studies
5. Cross-Sectional Studies: Snapshot analyses
6. Expert Opinion: Professional judgment and experience

Evidence Quality Assessment

• Internal Validity: How well does the study design support conclusions?
• External Validity: How generalizable are the results?
• Construct Validity: Do measures capture what they claim to measure?
• Statistical Conclusion Validity: Are statistical inferences appropriate?

Evidence Synthesis

• Weight of Evidence: Consider all available evidence, not just significant results
• Consistency: Look for consistent patterns across different analyses
• Biological/Business Plausibility: Ensure findings make logical sense
• Dose-Response: Look for logical relationships between variables

Uncertainty Communication

• Confidence Levels: Clearly state confidence in conclusions
• Limitations: Acknowledge what the analysis cannot determine
• Alternative Explanations: Consider other possible interpretations
• Future Research: Identify questions that require additional investigation

P-Hacking and Multiple Testing

• Problem: Testing many hypotheses until finding significance
• Solution: Pre-register hypotheses and adjust for multiple comparisons
• Prevention: Use structured hypothesis testing framework

Correlation vs. Causation

• Problem: Inferring causation from correlation
• Solution: Use appropriate causal inference methods
• Prevention: Always consider alternative explanations

Base Rate Neglect

• Problem: Ignoring prior probabilities when interpreting results
• Solution: Consider baseline rates and Bayesian approaches
• Prevention: Include context and historical data in analysis

Selection Bias

• Problem: Non-representative samples leading to biased conclusions
• Solution: Use random sampling and representativeness checks
• Prevention: Carefully consider data collection methodology

Survivorship Bias

• Problem: Analyzing only successful cases, ignoring failures
• Solution: Include all relevant cases in analysis
• Prevention: Actively look for missing data and excluded cases

Reproducible Workflows

• Version Control: Track all changes to data, code, and analysis
• Environment Management: Document and control computational environment
• Dependency Management: Track all software dependencies and versions
• Seed Setting: Use random seeds for reproducible random processes

Documentation Standards

• Analysis Documentation: Document every step of the analysis process
• Data Documentation: Describe data sources, transformations, and quality
• Decision Documentation: Record rationale for methodological choices
• Result Documentation: Clearly present findings with appropriate context

Internal Validation

• Code Review: Systematic review of analytical code and methods
• Result Verification: Independent verification of key findings
• Assumption Testing: Validate analytical assumptions
• Sensitivity Analysis: Test robustness of conclusions

External Validation

• Independent Replication: Ability for others to reproduce results
• Peer Review: Expert evaluation of methodology and conclusions
• Cross-Validation: Validation on independent datasets
• Real-World Testing: Validation against real-world outcomes

Hypothesis Generation

• Pattern Discovery: AI identifies potential relationships in data
• Literature Review: AI synthesizes relevant background knowledge
• Question Formulation: AI helps formulate testable hypotheses
• Experimental Design: AI suggests appropriate analytical approaches

Quality Control

• Bias Detection: AI identifies potential sources of bias
• Assumption Checking: AI validates analytical assumptions
• Method Selection: AI recommends appropriate statistical methods
• Result Interpretation: AI provides context for findings

Complementary Strengths

• Human Expertise: Domain knowledge, creativity, ethical judgment
• AI Capabilities: Pattern recognition, systematic checking, comprehensive analysis
• Combined Approach: Leverage both human insight and AI thoroughness
• Continuous Learning: Both human and AI learn from each analysis

Maintaining Human Oversight

• Critical Thinking: Human evaluation of AI suggestions
• Domain Expertise: Apply professional knowledge to interpret results
• Ethical Considerations: Ensure analyses meet ethical standards
• Final Judgment: Human responsibility for conclusions and decisions

Methodological Quality

• Design Appropriateness: Is the analytical approach suitable for the question?
• Statistical Power: Is the analysis adequately powered to detect effects?
• Bias Control: Are potential biases identified and controlled?
• Assumption Validity: Are analytical assumptions met and tested?

Result Quality

• Effect Size: What is the magnitude of observed effects?
• Confidence Intervals: How precise are the estimates?
• Statistical Significance: Are results statistically significant?
• Practical Significance: Are results practically meaningful?

Learning from Analysis

• Post-Analysis Review: Systematic evaluation of analytical choices
• Method Refinement: Continuous improvement of analytical methods
• Best Practice Development: Develop and share best practices
• Training and Education: Ongoing education in scientific methods

Community Standards

• Peer Review: Engage with professional community for feedback
• Standard Adoption: Follow established scientific standards
• Method Sharing: Share successful methodological approaches
• Open Science: Contribute to open science initiatives