Skip to content

Experiment Planning Flowchart

Run the Experiment Planning Flowchart Fullscreen

Edit in the p5.js Editor

About This MicroSim

"Designing an experiment can feel overwhelming," Sylvia admits, "but if you follow a logical decision process, it becomes much more manageable. Let me walk you through the key questions."

This interactive flowchart guides students through the decision-making process when designing an experiment, from defining the research question to choosing the appropriate design and applying key principles.

What You'll Learn

  • The key decisions in experimental design
  • How to choose between completely randomized, block, and matched pairs designs
  • When and how to apply the three principles
  • How to handle blinding decisions

How to Use

  1. Click any node to see detailed explanation
  2. Follow the flow from top (Start) to bottom (Final decisions)
  3. Note the decision diamonds (orange) where you answer Yes/No
  4. Process boxes (green) represent actions or designs to implement
  5. Hover over nodes to preview details

Understanding the Flowchart

Shape Color Meaning
Rounded rectangle Brown Start/End points
Diamond Orange Decision point (Yes/No)
Rectangle Green Process or action
Arrows Gray Flow direction

Key Decision Points

1. Significant Unit Variability?

First, assess whether your experimental units differ substantially from each other. - No: Use Completely Randomized Design (simple and effective) - Yes: Consider blocking to control for this variability

2. Can You Measure the Source?

If there is variability, can you identify and measure what causes it? - No: Increase sample size to overcome variation - Yes: You can block on this variable

3. Exactly 2 Treatments?

How many treatments are you comparing? - Yes (2 treatments): Consider Matched Pairs Design - No (3+ treatments): Use Randomized Block Design

4. Is Blinding Possible?

Can subjects and researchers be kept unaware of treatment assignment? - Yes: Implement single or double blinding - No: Document this limitation

"Acorn for your thoughts?" Sylvia asks. "Not every experiment can be perfectly blinded, and that's okay. The key is knowing your limitations and being honest about them."

Embedding This MicroSim

1
<iframe src="https://dmccreary.github.io/statistics-course/sims/experiment-planning-flowchart/main.html" height="502px" width="100%" scrolling="no"></iframe>

Lesson Plan

Learning Objectives

By the end of this activity, students will be able to:

  1. Follow a structured decision process for experimental design
  2. Determine when blocking is appropriate
  3. Choose between matched pairs and randomized block designs
  4. Identify when blinding is and is not possible

Target Audience

  • AP Statistics students (high school)
  • Research methods courses
  • Science fair participants

Prerequisites

  • Understanding of experimental design basics
  • Knowledge of completely randomized, block, and matched pairs designs
  • Concept of blinding

Classroom Activities

Activity 1: Follow the Flowchart (15 minutes)

Work through these scenarios using the flowchart:

  1. Testing a new fertilizer on plants from the same greenhouse batch
  2. Testing two diets on people with varying starting weights
  3. Testing three teaching methods on students of different ability levels

Activity 2: Design Documentation (20 minutes)

For a given research question, have students: 1. Navigate the flowchart 2. Document each decision and why 3. Write out the final experimental design 4. List variables to control

Activity 3: Flowchart Extension (15 minutes)

Discuss what additional decision points could be added: - Ethical considerations - Budget constraints - Time limitations - Available sample size

Common Mistakes to Address

  1. Skipping variability assessment: Always consider whether units differ
  2. Over-blocking: Only block on variables that actually affect the outcome
  3. Assuming blinding is always possible: Some treatments cannot be hidden
  4. Forgetting the three principles: Every design needs control, randomization, and replication

Assessment Questions

  1. A researcher wants to test whether a new app helps people drink more water. Users vary widely in their initial hydration habits. Walk through the flowchart: which design should they use?

  2. Why might a researcher choose to increase sample size rather than use blocking?

  3. At which point in the flowchart do you apply the three principles of experimental design?

  4. A study comparing surgery vs. medication cannot blind patients. How would you document this limitation?

References

  • Chapter 12: Experimental Design - Concepts: Completely Randomized Design, Randomized Block Design, Matched Pairs Design, Blinding
  • Experimental Design Checklist