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

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:

## 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:

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

## 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

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

## 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