Experiment Design

Design rigorous experimental protocols to validate research hypotheses.

Protocol Components

For each hypothesis, produce a complete protocol with:

Field	Description
Design	Experimental design type (RCT, quasi-experimental, longitudinal, in silico, etc.)
Independent Variable (IV)	What is manipulated
Dependent Variable (DV)	What is measured
Controls	Variables held constant
Sample Size	N per condition + power analysis (α=0.05, β=0.80, effect size)
Timeline	Phase-by-phase schedule
Protocol	Step-by-step procedure
Expected Outcome	What would confirm vs. refute the hypothesis

Power Analysis

Always include a power analysis. Standard parameters:

• α (Type I error rate): 0.05
• Power (1−β): 0.80
• Effect size: use domain-specific estimates or Cohen's conventions (small=0.2, medium=0.5, large=0.8)

Example: 30 simulations per condition (90 total, power analysis: α=0.05, β=0.80, η²=0.25)

Output Format

# 🧫 Experiment Design

## Strategy Overview
[2–3 sentences: how the experiments collectively test the hypotheses]

## Proposed Experiments

### Experiment 1: [Design Type]

**Tests Hypothesis:** [Exact hypothesis being tested]

| Parameter | Detail |
|-----------|--------|
| **Design** | [Design type] |
| **Sample Size** | [N per condition with power analysis] |
| **Timeline** | [X months: phase breakdown] |

**Independent Variables:** [What is manipulated]
**Dependent Variables:** [What is measured]
**Control Variables:** [What is held constant]

**Protocol:**
1. [Step 1]
2. [Step 2]
...

**Expected Outcome:** [What confirms the hypothesis. What would refute it.]

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Feasibility check

Before finalizing a protocol, consider:

• Can the required equipment/resources be co-located?
• Is the timeline realistic for the sample size?
• Are controls sufficient to rule out confounds?
• Does the analysis plan address multiple comparisons if >1 DV?