Research ScientistExperimental Design and MethodologyAdvancedSingle prompt

Pilot Study Design AI Prompt

Design a pilot study to test the feasibility of my planned main study before committing full resources. Main study plan: {{main_study_plan}} Key feasibility concerns: {{feasibil... Copy this prompt template, run it in your AI tool, and use related prompts to continue the workflow.

Prompt text
Design a pilot study to test the feasibility of my planned main study before committing full resources.

Main study plan: {{main_study_plan}}
Key feasibility concerns: {{feasibility_concerns}}

A pilot study is NOT a small version of the main study. It is a feasibility study with specific objectives that go beyond collecting preliminary effect size estimates.

1. Define the pilot's specific objectives:
   Tick each relevant objective for my study:
   - Recruitment feasibility: can I enroll participants at the required rate? What is the actual enrollment rate per week?
   - Randomization fidelity: does the randomization procedure work correctly in practice?
   - Protocol adherence: do participants and experimenters follow the protocol as written?
   - Attrition rate: what proportion of enrolled participants complete the study? Is this acceptable?
   - Treatment fidelity: is the treatment delivered as intended? Manipulation check performance?
   - Instrument performance: do the measures work well in this population? (Floor/ceiling effects, internal consistency, completion time)
   - Data quality: are there data entry errors, missing items, technical failures?
   - Procedure timing: how long does each study session actually take?
   - Acceptability: do participants find the study burdensome or the treatment acceptable?

2. What a pilot should NOT be used for:
   - Estimating effect sizes for power calculation (pilots are too small and estimates are unreliable)
   - Conducting statistical hypothesis tests on outcomes (severely underpowered)
   - Drawing any inferential conclusions about the intervention's efficacy
   These are common misuses of pilot data that inflate Type I error in subsequent studies.

3. Sample size for the pilot:
   - Typical guidance: 12–30 participants per arm for assessing feasibility
   - Size is driven by precision of feasibility estimates, not power for outcome effects
   - Example: to estimate a 50% completion rate within ±15%, you need approximately 40 participants

4. Progression criteria:
   - Define in advance the criteria that must be met for the main study to proceed
   - Example: 'Proceed if: (a) enrollment rate ≥ 5 participants/week, (b) protocol adherence ≥ 80%, (c) attrition ≤ 20%'
   - 'Stop-go-modify' criteria: proceed, modify protocol and re-pilot, or abandon

5. Reporting the pilot:
   - Report all feasibility metrics, not just those that look favorable
   - Be explicit that hypothesis testing on outcomes was not an objective

Return: pilot objectives checklist, sample size justification, progression criteria, and a pilot study protocol outline.

When to use this prompt

Use case 01

Use it when you want to begin experimental design and methodology work without writing the first draft from scratch.

Use case 02

Use it when you want a more consistent structure for AI output across projects or datasets.

Use case 03

Use it when you want prompt-driven work to turn into a reusable notebook or repeatable workflow later.

Use case 04

Use it when you want a clear next step into adjacent prompts in Experimental Design and Methodology or the wider Research Scientist library.

What the AI should return

The AI should return a structured result that covers the main requested outputs, such as Define the pilot's specific objectives:, Recruitment feasibility: can I enroll participants at the required rate? What is the actual enrollment rate per week?, Randomization fidelity: does the randomization procedure work correctly in practice?. The final answer should stay clear, actionable, and easy to review inside a experimental design and methodology workflow for research scientist work.

How to use this prompt

1

Open your data context

Load your dataset, notebook, or working environment so the AI can operate on the actual project context.

2

Copy the prompt text

Use the copy button above and paste the prompt into the AI assistant or prompt input area.

3

Review the output critically

Check whether the result matches your data, assumptions, and desired format before moving on.

4

Chain into the next prompt

Once you have the first result, continue deeper with related prompts in Experimental Design and Methodology.

Frequently asked questions

What does the Pilot Study Design prompt do?+

It gives you a structured experimental design and methodology starting point for research scientist work and helps you move faster without starting from a blank page.

Who is this prompt for?+

It is designed for research scientist workflows and marked as advanced, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Pilot Study Design is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.

Can I use this outside MLJAR Studio?+

Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.

What should I open next?+

Natural next steps from here are Confound Identification, Control Condition Designer, Full Study Design Chain.