Selecting an Experimental Design
Isabella Lopez
8 min read
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Study Guide Overview
This study guide covers experimental design for the AP Statistics exam, focusing on completely randomized, blocking, and matched pairs designs. It explains how and when to use each design, provides practice problems and solutions, highlights common mistakes, and offers memory aids and final exam tips. It also includes practice multiple-choice and free-response questions with answers and scoring guidelines.
#Experimental Design: Your Guide to AP Stats Success 🚀
Hey there, future AP Stats superstar! Let's break down experimental design so you're ready to rock the exam. Remember, choosing the right design is key to getting valid results. Let's dive in!
#The Big Three Experimental Designs
These are the bread and butter of experimental design. Know them well!
# Completely Randomized Design
In a completely randomized design, experimental units are assigned to treatments randomly. Each unit has an equal chance of being in any treatment group. It's the simplest design, great for when you don't suspect other lurking variables.
- How it works: Think of drawing names from a hat. Each participant has an equal chance of being assigned to any group.
- When to use it: When you have a fairly homogenous group and no specific variables you need to control for.
- Example: Testing the effectiveness of a new fertilizer on plants. Randomly assign plants to different fertilizer groups.
# Blocking Design
In a blocking design, you first group your experimental units into blocks based on a variable that might affect the response. Then, you randomize within each block.
- How it works: Like sorting socks by color before matching them. You group similar units together, then randomize treatments within those groups.
- When to use it: When you know a variable (the blocking variable) could influence your results. This helps reduce variability within treatment groups.
- Example: Testing a new drug, but you know age affects drug response. Block by age group, then randomize treatment within each age group.
# Matched Pairs Design
A matched pairs design is a special type of blocking where you pair up similar units (or use the same unit twice) and then randomly assign treatments within each pair.
- How it works: Think of twins. You pair them up because they're very similar, then give each twin a different treatment. Or, you could give the same person two different treatments over time.
- When to use it: When you have two treatments and want to compare them on very similar units. Great for reducing variability due to individual differences....
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