/hypothesis-generation

name: hypothesis-generation
description: "Structured hypothesis formulation: turn observations into testable hypotheses with predictions, propose mechanisms, design experiments. Follows the scientific method. Use scientific-brainstorming for open ideation; hypogenic for automated LLM hypothesis testing on datasets."
license: CC-BY-4.0

Scientific Hypothesis Generation

Overview

Hypothesis generation is a systematic process for developing testable mechanistic explanations from observations. This knowhow covers the full cycle: from understanding a phenomenon through literature synthesis, generating competing hypotheses, evaluating hypothesis quality, designing experimental tests, and formulating testable predictions.

Key Concepts

1. Hypothesis vs Observation vs Prediction

• **Observation**: A factual statement about what was measured or seen (e.g., "Drug X reduces tumor size in mice")
• **Hypothesis**: A proposed mechanistic explanation for the observation (e.g., "Drug X inhibits angiogenesis via VEGF pathway blockade, reducing tumor nutrient supply")
• **Prediction**: A testable consequence of the hypothesis (e.g., "VEGF levels should decrease after Drug X treatment; tumors in VEGF-knockout mice should show no additional effect")

Good hypotheses are mechanistic (explain HOW/WHY), not descriptive (restate WHAT).

2. Hypothesis Quality Criteria

| Criterion | Definition | Example of Strong | Example of Weak | |-----------|-----------|-------------------|-----------------| | **Testability** | Can be empirically investigated | "Protein X binds to receptor Y" (can test with co-IP) | "Life force drives cellular growth" (untestable) | | **Falsifiability** | Specific observations would disprove it | "If X is absent, effect disappears" | "X contributes to the effect somehow" | | **Parsimony** | Simplest explanation fitting the evidence | Single mechanism | Multi-step chain without evidence | | **Explanatory Power** | Accounts for observed patterns | Explains dose-response and tissue specificity | Explains only one observation | | **Scope** | Range of phenomena covered | Applies across related systems | Limited to single dataset | | **Consistency** | Aligns with established knowledge | Consistent with known pathway biology | Contradicts thermodynamics | | **Novelty** | Offers new insight | Proposes unexplored mechanism | Restates established knowledge |

3. Levels of Mechanistic Explanation

Hypotheses can operate at different scales. Strong hypothesis sets include explanations at multiple levels:

• **Molecular**: Protein interactions, gene regulation, enzymatic activity
• **Cellular**: Signaling pathways, cell fate decisions, metabolic changes
• **Tissue/Organ**: Microenvironment, cell-cell communication, organ function
• **Organismal**: Systemic responses, physiological adaptation
• **Population**: Evolutionary pressures, epidemiological patterns

Decision Framework

What is your starting point?
├── Specific observation / data → Follow the full 8-step Workflow below
├── Broad research question → Start with Step 2 (literature search) to narrow scope
├── Existing hypothesis to refine → Start at Step 5 (evaluate quality) and iterate
└── Need creative ideation first → Use scientific-brainstorming skill, then return here

| Starting Situation | Approach | Key Steps | |-------------------|----------|-----------| | Unexpected experimental result | Phenomenon-driven | Steps 1→2→3→4 (focus on competing explanations) | | Literature gap identified | Gap-driven | Steps 2→3→4→5 (focus on novelty criterion) | | Cross-domain analogy noticed | Analogy-driven | Steps 1→4→5→6 (focus on translating mechanism) | | Contradictory findings in literature | Conflict-driven | Steps 2→3→4→7 (focus on discriminating predictions) | | Large dataset patterns | Data-driven | Use hypogenic first, then Steps 5→6→7 here |

Best Practices

1. **Always generate competing hypotheses (3–5)**: A single hypothesis is a confirmation trap. Multiple competing explanations force you to design experiments that discriminate between alternatives, not just confirm your favorite.

2. **Start with mechanism, not correlation**: "X is associated with Y" is not a hypothesis. "X causes Y via mechanism Z" is. Always include the mechanistic link (HOW the cause produces the effect).

3. **Make predictions that differ between hypotheses**: The most valuable predictions are those where Hypothesis A predicts outcome X and Hypothesis B predicts outcome Y. This is called a "crucial experiment" — design your tests around these discriminating predictions.

4. **Ground every hypothesis in evidence**: Cite existing literature for each hypothesis. "It is known that pathway X can regulate process Y [Author, 2023]; therefore, we hypothesize that..." Unsupported hypotheses are speculation, not science.

5. **State falsification criteria explicitly**: For each hypothesis, write "This hypothesis would be falsified if..." before designing experiments. If you cannot state falsification criteria, the hypothesis is untestable.

6. **Consider the null hypothesis**: The simplest explanation — that there is no novel mechanism and observed effects are due to known processes, artifact, or chance — should always be included as one of the competing hypotheses.

7. **Scale predictions quantitatively when possible**: "Expression should increase" is weaker than "Expression should increase 2–5 fold (based on known pathway kinetics)." Quantitative predictions enable power analysis for experimental design.

Common Pitfalls

1. **Confirmation bias in hypothesis selection**: Generating one "main" hypothesis and 2-3 weak alternatives to make the main one look good. *How to avoid*: Generate hypotheses independently, then rank them by quality criteria. Have someone else review whether alternatives are genuinely competitive.

2. **Untestable "just-so" stories**: Hypotheses that sound plausible but cannot be empirically tested with current te