Section outline
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Welcome to Unlocking Experimental Design: An Introduction!
This lesson is designed to take you on an engaging journey through the fundamental concepts of experimental design, helping you understand how to systematically investigate cause-and-effect relationships. Throughout the lesson, you’ll gain valuable insights into identifying variables, minimizing bias, and optimizing your experiment's validity and reliability.
My name is Dr. Nurulhuda Ramli, and I will be your course manager. Feel free to reach out to me you have questions or need assistance.
Before diving into the lesson, let’s take a moment to review some important aspects that will help you navigate through the course successfully.
Subtopics Covered in This Lesson
In this lesson, we’ll explore the following key areas:
- Principle of Experimental Design
- Basic Statistical Concept
Learning Outcomes
By the end of this session, you will be able to:
- Identify the basic principles of experimental design
- Explain and apply key statistical concepts to analyze and interpret data
Student Learning Time (SLT)
To help you plan your time effectively, here’s an estimate of how long each section may take:
- Reading Main Materials: 1 hour
- Interactive Self Learning Activities: 1 hour
- Self-Assessment: 0.5 hour
Feel free to work through the material at your own pace. It’s important to allocate enough time for reflection and practice to fully absorb the concepts.
Let’s get started on this exciting journey of discovery!
Course Manager,
Dr. Nurulhuda Ramli
⚙️🔍⏩
"The purpose of statistical science is to provide an objective basis for the analysis of problems in which the data depart from the laws of exact causality."
- D. J. Finney, An Introduction to Statistical Science in Agriculture
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How Do Scientists Investigate Questions?
Have you ever found yourself wondering about experiments you encountered back in high school or junior high? Do you remember the steps? You might recall deciding on a phenomenon to study, manipulating factors, and measuring the response to understand the outcome.
For example, when running an experiment, you might have to manipulate one factor—like temperature—while holding others constant to ensure no external conditions affect your results. If changing the factor leads to a change in the outcome, you’ve uncovered a cause-and-effect relationship.
Think about how many factors you consider when conducting an experiment. Sometimes there’s only one, perhaps in a simple comparative experiment with a treatment group and a control group. But other experiments, such as baking a cake, involve many more factors—baking time, temperature, moisture, ingredients, and even the method of preparation. Someone had to experiment with all these variables to perfect the recipe.
Now imagine if you varied just one factor while keeping the rest constant to see what would happen. It’s an approach, but it’s not very efficient. In this course, we’ll explore how to design experiments more effectively by understanding the role of multiple factors and how they interact.
By the end of the course, you’ll be able to design robust experiments and confidently investigate the world around you.
🤗📑 Let’s start the journey! 💻🌟
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Subtopic 1: Principle of Experimental Design
Do you remember learning about this back in high school or junior high even? What were those steps again? Decide what phenomenon you wish to investigate. Specify how you can manipulate the factor and hold all other conditions fixed, then measure your chosen response variable at several (at least two) settings of the factor under study. If changing the factor causes the phenomenon to change, then you conclude that there is indeed a cause-and-effect relationship at work. This is the concept of experimental design that we will address in this course.All of the main learning sources for this topic is supplied here. To navigate to the previous or next page, please use the UP 🔼 and DOWN 🔽 arrows in the upper right corner of the display. You are welcome to explore at your own speed. Enjoy reading and exploring!
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Subtopic 2: Basic Statistical Concept
In this topic, we consider experiments to compare two conditions (sometimes called treatments). These are often called simple comparative experiments. We begin with an example of an experiment performed to determine whether two different formulations of a product give equivalent results. The discussion leads to a review of several basic statistical concepts, such as graphical description of variability, probability distributions and mean, variance, and expected values.
The main learning material for this subtopic can be found here. Help yourself to navigate the pages at your own pace.
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View Make forum posts: 1
Imagine you are a researcher tasked with designing a study to test the effect of instructor communication style on participants' performance in spin classes. Participants will be assigned to one of two groups:
- Treatment Group: An instructor who yells at participants during the class.
- Control Group: An instructor who speaks to participants in a normal, calm manner (referred to as the 'standard' or 'basic' treatment).
Your goal is to set up a Completely Randomized Design (CRD) for this experiment.
Key Question:
- How would you structure a CRD for this study?
- What considerations must be made to ensure randomness and control for potential biases?
- How would you assign participants to these groups, and what factors (e.g., number of participants, class schedules, or baseline fitness levels) should be controlled or balanced?
Share your approach and discuss any challenges you foresee in setting up this CRD effectively.
*Learning outcome from this activity: Able to identify basic principle of experimental design
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Understanding Variability in Experimental Design
It is important to understand the concept of variability in experimental design.
Please watch the following video on variability. After watching, identify different types of variability that may affect the outcomes of an experiment.
(Source: Youtube)*Learning outcome from this activity: Able to explain and apply key statistical concepts in experimental design
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In this section, you will have the opportunity to test your acquired knowledge throughout the lesson. This questions will assess your understanding in achieving the following learning outcomes:
✔️Identify the basic principles of experimental design
✔️Explain and apply key statistical concepts to analyze and interpret data.
Don't miss the chance to demonstrate everything you've learned so far!
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Watch the following video to enhance your understanding of the basic concepts of experimental design.
(Source: Youtube)
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GlossaryView Make entries: 1
The following key terms provides a brief definition of key terms and concepts as they are used in this lesson.
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LESSON RECAP 📑
In this topic, we covered essential concepts for experimental design and statistical analysis. We examined the principles of experimental design, focusing on randomization, replication, and control to ensure reliable and valid results. We also explored basic statistical concepts, including graphical methods for visualizing data variability, various probability distributions, and fundamental measures such as the mean, variance, and expected value. These concepts are crucial for effectively analyzing experimental data and drawing informed conclusions.
As we move forward, be prepared to build on this knowledge with new concepts that will deepen your understanding. Stay curious and engaged as we explore the next topic together.
Happy studying, and let’s get ready for the exciting material ahead!
🤗💻🌟
Course Manager,
Dr. Nurulhuda Ramli
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