hypothesis-testing

name	hypothesis-testing
description	Guides scientific hypothesis development and testing methodology. Use when formulating research questions, developing testable hypotheses, designing experiments, or evaluating research approaches. Triggers on phrases like "hypothesis", "test if", "experiment design", "research question", "how would I test", "is it true that".
tools	["WebSearch","WebFetch","Read","Grep","Glob"]

Hypothesis Testing Workflow

This skill guides you through rigorous hypothesis development and testing methodology.

Phase 1: Observation and Question

Starting Point Analysis

• What observation or phenomenon prompted this inquiry?
• What patterns or anomalies are you seeing?
• What existing knowledge is relevant?

Research Question Formulation

Good research questions are:

• Focused: Specific enough to answer
• Researchable: Can be investigated empirically
• Complex: Requires analysis, not just facts
• Arguable: Has multiple possible answers

Question Types

Type	Example	Hypothesis Style
Descriptive	"What is X?"	Not hypothesis-driven
Relational	"Is X related to Y?"	Correlation hypothesis
Causal	"Does X cause Y?"	Causal hypothesis
Comparative	"Is X different from Y?"	Difference hypothesis

CHECKPOINT: Confirm research question with user.

Phase 2: Hypothesis Construction

Hypothesis Components

If [independent variable/condition]
Then [dependent variable/outcome]
Because [theoretical mechanism]

Null vs Alternative Hypothesis

• H₀ (Null): No effect/relationship exists
• H₁ (Alternative): Effect/relationship exists

Example:

• H₀: Training method has no effect on performance
• H₁: Training method improves performance

Hypothesis Quality Check

• Is it testable with available methods?
• Is it falsifiable (can be proven wrong)?
• Does it make specific predictions?
• Is it parsimonious (simplest explanation)?
• Is it consistent with existing knowledge?
• Does it specify the mechanism?

Phase 3: Variable Mapping

Variable Identification

Variable	Type	Operationalization
[Name]	Independent (IV)	[How measured/manipulated]
[Name]	Dependent (DV)	[How measured]
[Name]	Control	[How held constant]
[Name]	Confound	[Potential interference]
[Name]	Mediator	[Explains mechanism]
[Name]	Moderator	[Affects strength]

Operationalization Criteria

For each variable:

• Concrete, observable indicators
• Reliable measurement method
• Valid representation of construct
• Appropriate scale (nominal, ordinal, interval, ratio)

Phase 4: Prediction Generation

Specific Predictions

From your hypothesis, derive:

1. If H₁ true: [Specific observable outcome]
2. If H₀ true: [Expected null result]
3. Effect direction: [Increase/decrease/differ]
4. Effect magnitude: [Expected size]

Boundary Conditions

• Under what conditions should hypothesis hold?
• Where might it not apply?
• What would moderate the effect?

CHECKPOINT: Validate predictions align with user's research goals.

Phase 5: Design Selection

Experimental vs Observational

Can you manipulate the IV?
├── Yes → Experimental design
│   ├── Random assignment possible? → True experiment
│   └── No random assignment? → Quasi-experiment
└── No → Observational design
    ├── Over time? → Longitudinal
    └── Single point? → Cross-sectional

Design Options

Design	Strengths	Limitations
RCT	Causal inference	Artificial, expensive
Quasi-experiment	More feasible	Weaker causal claims
Cohort	Temporal sequence	Attrition, time
Case-control	Efficient for rare outcomes	Recall bias
Cross-sectional	Quick, inexpensive	No causation

Control Strategies

Threat	Control Method
Selection bias	Random assignment, matching
History	Control group, isolation
Maturation	Control group, short duration
Testing effects	Control group, alternate forms
Instrumentation	Standardization, calibration

Phase 6: Confound Mitigation

Confound Analysis

For each potential confound:

1. How could it affect the DV?
2. How might it correlate with the IV?
3. What's the mitigation strategy?

Mitigation Strategies

Strategy	How It Works
Random assignment	Distributes confounds equally
Matching	Pairs similar participants
Statistical control	Adjust in analysis
Counterbalancing	Vary order of conditions
Blinding	Remove bias from knowledge
Standardization	Same procedures for all

Phase 7: Falsifiability Statement

Define Falsification Criteria

Specify exactly what results would falsify H₁:

• What outcome pattern rejects the hypothesis?
• What effect size is too small to matter?
• What statistical threshold applies?

Pre-registration Elements

• Hypothesis (before seeing data)
• Analysis plan (before seeing data)
• Sample size justification
• Exclusion criteria
• Success/failure criteria

Phase 8: Documentation

Output Structure

# Hypothesis Development: [Topic]

## Research Question
[Clearly stated question]

## Hypotheses
- H₀: [Null hypothesis]
- H₁: [Alternative hypothesis]
- Mechanism: [Why we expect this]

## Variables
| Variable | Type | Operationalization |
|----------|------|-------------------|
| [Name] | [Type] | [Definition] |

## Predictions
1. If H₁: [Expected outcome]
2. If H₀: [Expected outcome]
3. Effect size: [Expected magnitude]

## Design
- Type: [Design name]
- Justification: [Why this design]

## Confounds and Controls
| Confound | Risk | Mitigation |
|----------|------|------------|
| [Name] | [Level] | [Strategy] |

## Falsification Criteria
[Specific conditions that would reject H₁]

## Feasibility Notes
- Resources needed: [List]
- Ethical considerations: [List]
- Timeline estimate: [Estimate]