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Understanding Experimental Design: Key Concepts and Practical Applications in Research

This document explores essential aspects of experimental design, including terminology, precautions, and factorial design. Emphasizing the importance of controls, randomization, and repetition, it discusses how to determine significant effects of variables such as temperature and pressure on various responses. Key topics include numeric and categorical factors, the nature of interactions, and the relevance of proper experimental setup to ensure valid results. It also presents practical examples and projects for laboratory investigations, promoting a thorough understanding of experimental methodologies.

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Understanding Experimental Design: Key Concepts and Practical Applications in Research

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  1. Experimental Design Anthony E. Butterfield CH EN 4903-1 “Research is what I'm doing when I don't know what I'm doing. ” ~ Wernher Von Braun (1912 – 1977)

  2. Announcements • Schedule preliminary exam before next lab period so you can get started Wednesday. • Start writing now. • Introduction, theory, materials and equipment table… • Draft of a new Projects Lab web page:http://www.che.utah.edu/projectslab/ • Has updated SOPs for some of your experiments.

  3. Experimental Design • Terminology. • Precautions. • Factorial Design of Experiments. • Put It to Use.

  4. Terminology • Numeric Factors: Variable inputs to a process. For example: pressure, temperature… • Categorical Factors: Discrete inputs to a process.For example: equipment model, chemical substance… • Responses: Effects of changes in factors.For example: Reaction rate increases with temperature and catalyst. • Interactions: The influence of factors on each other.For example: (drinking, driving) vs (drunk driving)

  5. Precautions • Controls. • Why spend the time and resources? • How would you decide if changing a factor gave results significantly different from control experiments? • Randomizing. • Sampling. • When time is a possible factor. • Blindness. • You can be easily fooled by yourexpectations, wants, and pre-conscious processing of data. • Repetition. • In your lab and others.

  6. Experiments for Your Projects • Group A – Immortal Yeast? • Group B – Dirty Hydrogels? • Group C – The Purple CSTR? • Group D – CO2 Absorber? • Group E – Stage Control?

  7. Experimental Design • In my lab, we want to know the effect of temperature, pressure, and time on microbubble volume. • One option:

  8. What Have We Actually Pinned Down? • 3 factors. • 18 experiments. • So, what is the problem? • Repeatability? • Effects? • Interactions?

  9. Factorial Design • 2-Level Factorial with center points. • Some information about repeatability. • Some information about curvature. • Box-Behnken • 3 Factors. • 3 Levels. • 12 Experiments plus center points. • Spherical and so extrapolation is less prone to error. • 2-Level Factorial • 2 Levels, x • 3 Factors, y • 8 tests, (xy) • Larger volume analyzed. • Effect of factors at multiple surfaces. • Some information on interactions.

  10. Why Ignore This Advice?

  11. Competitive Experimental Design • What are the factors? • Numeric or categorical? • Are there interactions? • Each team can perform10 experiments. • Develop a model. • Take 15 shots at the opposing team’s target.

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