# Hypothesis Generation > Formulate research hypotheses using structured frameworks. Use when developing research questions, designing experiments, or planning studies with testable predictions. Use when this capability is needed. - Skill: `tomevault-io/hypothesis-generation-2` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/hypothesis-generation-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/hypothesis-generation-2/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/hypothesis-generation-2 --- # Hypothesis Generation Structured frameworks for developing research hypotheses and experimental designs. ## When to Use - Starting a new research project - Developing research questions - Planning experiments - Generating testable predictions - Exploring competing explanations ## Hypothesis Framework ### Good Hypothesis Characteristics A strong research hypothesis should be: 1. **Specific**: Clear, precise statement 2. **Testable**: Can be validated with data 3. **Falsifiable**: Can potentially be proven wrong 4. **Grounded**: Based on prior knowledge/theory 5. **Novel**: Adds something new to the field ### Hypothesis Types | Type | Description | Example | |------|-------------|---------| | **Descriptive** | Describes a phenomenon | "LLMs exhibit X behavior on task Y" | | **Relational** | Proposes relationship | "Factor A correlates with outcome B" | | **Causal** | Claims causation | "Intervention X causes improvement Y" | | **Comparative** | Compares conditions | "Method A outperforms method B on task C" | | **Mechanistic** | Explains how/why | "Effect X occurs because of mechanism Y" | ## Hypothesis Development Process ### Step 1: Identify the Gap From your literature review, identify: - What is known - What is unknown or unclear - What is contradictory Document the gap: ```markdown ## Research Gap **Known**: [What prior work has established] **Unknown**: [What remains to be discovered] **Our Focus**: [Which unknown we address] ``` ### Step 2: Generate Initial Hypotheses Use these prompts: - "If [assumption] is true, then we should observe [prediction]" - "Based on [theory/observation], we expect [outcome]" - "Contrary to [current belief], we propose [alternative]" Generate multiple hypotheses (aim for 3-5 initially). ### Step 3: Develop Competing Hypotheses For each hypothesis, identify: - **Alternative explanations**: What else could explain the same observation? - **Null hypothesis**: What if there's no effect? - **Opposite hypothesis**: What if the effect is reversed? ### Step 4: Operationalize Convert abstract hypothesis to concrete, measurable terms: | Abstract | Operationalized | |----------|-----------------| | "LLMs understand X" | "GPT-4 achieves >80% accuracy on benchmark Y" | | "Method A is better" | "Method A improves F1 by >5% over baseline B" | | "Training affects X" | "Models trained with X show Y behavior increase" | ### Step 5: Design Tests For each hypothesis, define: - **Data**: What data is needed? - **Method**: How will you test? - **Metrics**: What measures success/failure? - **Threshold**: What counts as support/rejection? ## Competing Hypotheses Framework ### Template ```markdown ## Research Question [Your main question] ### Hypothesis 1: [Name] **Statement**: [Formal hypothesis] **Rationale**: [Why this might be true] **Prediction**: [What we expect to observe] **Test**: [How to test] ### Hypothesis 2: [Alternative] **Statement**: [Formal hypothesis] **Rationale**: [Why this might be true] **Prediction**: [What we expect to observe] **Test**: [How to test] ### Hypothesis 3: [Null] **Statement**: There is no significant effect **Prediction**: No difference between conditions **Test**: Statistical significance testing ### Decision Matrix | Outcome | Supports H1 | Supports H2 | Supports H3 | |---------|-------------|-------------|-------------| | [Result A] | Yes | No | No | | [Result B] | No | Yes | No | | [Result C] | No | No | Yes | ``` ## Experimental Design ### Variables | Type | Definition | Example | |------|------------|---------| | **Independent (IV)** | What you manipulate | Model type, training data | | **Dependent (DV)** | What you measure | Accuracy, F1, latency | | **Controlled** | Held constant | Prompt template, temperature | | **Confounding** | Could affect DV | Data contamination, model size | ### Design Types **Between-subjects**: Different conditions get different treatments - Pros: No carryover effects - Cons: Need more samples, individual differences **Within-subjects**: Same subject gets all treatments - Pros: Controls individual differences - Cons: Order effects, fatigue **Factorial**: Multiple IVs crossed - Pros: Tests interactions - Cons: More conditions needed ### Control Strategies 1. **Baseline comparison**: Compare against known baseline 2. **Ablation study**: Remove components to test necessity 3. **Randomization**: Random assignment to conditions 4. **Counterbalancing**: Vary order across subjects/trials ## Prediction Documentation ### Template for Each Hypothesis ```markdown ## Hypothesis: [Name] ### Formal Statement [If X, then Y under conditions Z] ### Background [Why we think this might be true] ### Predictions #### Primary Prediction - **Measure**: [What to measure] - **Expected outcome**: [Specific prediction] - **Threshold for support**: [Quantitative criterion] #### Secondary Predictions 1. [Additional prediction 1] 2. [Additional prediction 2] ### Potential Confounds - [Confound 1]: [How to address] - [Confound 2]: [How to address] ### What Would Falsify This? [Specific outcomes that would reject hypothesis] ``` ## Common Pitfalls ### Avoid These 1. **Vague hypotheses**: "Method A is good" → "Method A achieves >X on benchmark Y" 2. **Unfalsifiable claims**: "LLMs may sometimes..." → "LLMs will show X in condition Y" 3. **Post-hoc hypothesizing**: Generating hypothesis after seeing data 4. **Confirmation bias**: Only looking for supporting evidence 5. **Missing null hypothesis**: Not considering "no effect" possibility ### Warning Signs - Hypothesis can explain any outcome - No clear way to measure - Based on single observation - Ignores contradictory evidence - No alternative hypotheses considered ## Quality Checklist - [ ] Hypothesis is specific and clear - [ ] Hypothesis is testable with available resources - [ ] Hypothesis is falsifiable - [ ] Hypothesis is grounded in prior work - [ ] Alternative hypotheses identified - [ ] Null hypothesis specified - [ ] Variables operationalized - [ ] Confounds identified and addressed - [ ] Success/failure criteria defined - [ ] Predictions documented before experimentation ## References See `references/` folder for: - `hypothesis_templates.md`: Templates for different research types --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/chicagohai) — claim your Tome and manage your conversions.