gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/hypothesis-generation/references/hypothesis_quality_criteria.md

Hypothesis Quality Criteria

Framework for Evaluating Scientific Hypotheses

Use these criteria to assess the quality and rigor of generated hypotheses. A robust hypothesis should score well across multiple dimensions.

Core Criteria

1. Testability

Definition: The hypothesis can be empirically tested through observation or experimentation.

Evaluation questions:

• Can specific experiments or observations test this hypothesis?
• Are the predicted outcomes measurable?
• Can the hypothesis be tested with current or near-future methods?
• Are there multiple independent ways to test it?

Strong testability examples:

• "Increased expression of protein X will reduce cell proliferation rate by >30%"
• "Patients receiving treatment Y will show 50% reduction in symptom Z within 4 weeks"

Weak testability examples:

• "This process is influenced by complex interactions" (vague, no specific prediction)
• "The mechanism involves quantum effects" (if no method to test quantum effects exists)

2. Falsifiability

Definition: Clear conditions or observations would disprove the hypothesis (Popperian criterion).

Evaluation questions:

• What specific observations would prove this hypothesis wrong?
• Are the falsifying conditions realistic to observe?
• Is the hypothesis stated clearly enough to be disproven?
• Can null results meaningfully falsify the hypothesis?

Strong falsifiability examples:

• "If we knock out gene X, phenotype Y will disappear" (can be falsified if phenotype persists)
• "Drug A will outperform placebo in 80% of patients" (clear falsification threshold)

Weak falsifiability examples:

• "Multiple factors contribute to the outcome" (too vague to falsify)
• "The effect may vary depending on context" (built-in escape clauses)

3. Parsimony (Occam's Razor)

Definition: Among competing hypotheses with equal explanatory power, prefer the simpler explanation.

Evaluation questions:

• Does the hypothesis invoke the minimum number of entities/mechanisms needed?
• Are all proposed elements necessary to explain the phenomenon?
• Could a simpler mechanism account for the observations?
• Does it avoid unnecessary assumptions?

Parsimony considerations:

• Simple ≠ simplistic; complexity is justified when evidence demands it
• Established mechanisms are "simpler" than novel, unproven ones
• Direct mechanisms are simpler than elaborate multi-step pathways
• One well-supported mechanism beats multiple speculative ones

4. Explanatory Power

Definition: The hypothesis accounts for a substantial portion of the observed phenomenon.

Evaluation questions:

• How much of the observed data does this hypothesis explain?
• Does it account for both typical and atypical observations?
• Can it explain related phenomena beyond the immediate observation?
• Does it resolve apparent contradictions in existing data?

Strong explanatory power indicators:

• Explains multiple independent observations
• Accounts for quantitative relationships, not just qualitative patterns
• Resolves previously puzzling findings
• Makes sense of seemingly contradictory results

Limited explanatory power indicators:

• Only explains part of the phenomenon
• Requires additional hypotheses for complete explanation
• Leaves major observations unexplained

5. Scope

Definition: The range of phenomena and contexts the hypothesis can address.

Evaluation questions:

• Does it apply only to the specific case or to broader situations?
• Can it generalize across conditions, species, or systems?
• Does it connect to larger theoretical frameworks?
• What are its boundaries and limitations?

Broader scope (generally preferable):

• Applies across multiple experimental conditions
• Generalizes to related systems or species
• Connects phenomenon to established principles

Narrower scope (acceptable if explicitly defined):

• Limited to specific conditions or contexts
• Requires different mechanisms in different settings
• Context-dependent with clear boundaries

6. Consistency with Established Knowledge

Definition: Alignment with well-supported theories, principles, and empirical findings.

Evaluation questions:

• Is it consistent with established physical, chemical, or biological principles?
• Does it align with or reasonably extend current theories?
• If contradicting established knowledge, is there strong justification?
• Does it require violating well-supported laws or findings?

Levels of consistency:

• Fully consistent: Applies established mechanisms in new context
• Mostly consistent: Extends current understanding in plausible ways
• Partially inconsistent: Contradicts some findings but has explanatory value
• Highly inconsistent: Requires rejecting well-established principles (requires exceptional evidence)

7. Novelty and Insight

Definition: The hypothesis offers new understanding beyond merely restating known facts.

Evaluation questions:

• Does it provide new mechanistic insight?
• Does it challenge assumptions or conventional wisdom?
• Does it suggest unexpected connections or relationships?
• Does it open new research directions?

Novel contributions:

• Proposes previously unconsidered mechanisms
• Reframes the problem in a productive way
• Connects disparate observations
• Suggests non-obvious testable predictions

Note: Novelty alone doesn't make a hypothesis valuable; it must also be testable, parsimonious, and explanatory.

Comparative Evaluation

When evaluating multiple competing hypotheses:

Trade-offs and Balancing

Hypotheses often involve trade-offs:

• More parsimonious but less explanatory power
• Broader scope but less testable with current methods
• Novel insights but less consistent with current knowledge

Evaluation approach:

• No hypothesis needs to be perfect on all dimensions
• Identify each hypothesis's strengths and weaknesses
• Consider which criteria are most important for the specific phenomenon
• Note which hypotheses are most immediately testable
• Identify which would be most informative if supported

Distinguishability

Key question: Can experiments distinguish between competing hypotheses?

• Identify predictions that differ between hypotheses
• Prioritize hypotheses that make distinct predictions
• Note which experiments would most efficiently narrow the field
• Consider whether hypotheses could all be partially correct

Common Pitfalls

Untestable Hypotheses

• Too vague to generate specific predictions
• Invoke unobservable or unmeasurable entities
• Require technology that doesn't exist

Unfalsifiable Hypotheses

• Built-in escape clauses ("may or may not occur")
• Post-hoc explanations that fit any outcome
• No specification of what would disprove them

Overly Complex Hypotheses

• Invoke multiple unproven mechanisms
• Add unnecessary steps or entities
• Complexity not justified by explanatory gains

Just-So Stories

• Plausible narratives without testable predictions
• Explain observations but don't predict new ones
• Impossible to distinguish from alternative stories

Practical Application

When generating hypotheses:

1. Draft initial hypotheses focusing on mechanistic explanations
2. Apply quality criteria to identify weaknesses
3. Refine hypotheses to improve testability and clarity
4. Develop specific predictions to enhance testability and falsifiability
5. Compare systematically across all criteria
6. Prioritize for testing based on distinguishability and feasibility

Remember: The goal is not a perfect hypothesis, but a set of testable, falsifiable, informative hypotheses that advance understanding of the phenomenon.