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Experimental Design
Common Core: S.IC.3

Experimental Design

Author: Sophia Tutorial
Description:

Differentiate the three principles of experimental design.

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Tutorial

what's covered
In this tutorial, you're going to learn about the principles of experimental design.

  1. Components of Experimental Design
    1. Control
    2. Randomization
    3. Replication


1. Components of Experimental Design

Experimental design refers to how an experiment is carried out. Many experimental designs include a control group and a treatment group to compare effects of treatment (exercise, drug, video watching, etc.). You can have a good design of an experiment or a poor design of an experiment.

Good experimental design will have these three components:

  1. Control
  2. Randomization
  3. Replication
terms to know

Experimental Design
The way in which an experiment is carried out. A good design has key elements of randomization, replication, and control.
Treatment
Something the researchers administer to the subjects or experimental units.

1a. Control
Control means holding everything else besides what you're trying to measure constant. The purpose is to determine whether or not your treatment is effective. In other words, if there is an observable difference between groups, is it due to the treatments or due to a confounding variable? It is important to control all other variables to help limit confounding.

Source: This work is adapted from Sophia author Jonathan Osters.

Experimental Design (2:30)

Video Transcription

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[MUSIC PLAYING] Hello. Let's take a look at a real-life example using experimental design. Suppose a farmer wants to try a new fertilizer in the fields. The three components of experimental design can be used to determine if the new fertilizer is better than the old one. Here's how it would work.

The first thing the farmer would do is determine the control by selecting 10 fields with similar soil nutrients, sunlight, and water. These are all variables that could affect the crop growth. The farmer would then apply the old fertilizer to five fields and the new fertilizer to the other five. By keeping the control elements consistent across the 10 fields, the differences between them can be isolated and attributed to either the old or the new fertilizer.

Next, the farmer takes randomization into account by randomly assigning which five fields will get the new fertilizer. While the fields selected were as similar as possible, there may be an unknown variable that was not accounted for. Perhaps some fields had moles underground. And that would affect how the crops grow.

By randomly assigning treatments, the farmer should get some fields with moles using the new fertilizer and some fields with moles using the old fertilizer. Randomization smooths out those effects that unknown variables might bring into the equation.

Lastly, the farmer understands the significance of repeated results rather than a one-off result. Say the farmer was only able to find two fields similar to each other and randomly assigned one for the new fertilizer and one for the old. It is possible in that case that the field with the old fertilizer does very well just by random chance. This would make it seem like the new fertilizer is not effective when perhaps it is.

Or the opposite could happen where it seems like the new fertilizer is effective when it's not. So it would always be better to randomly assign 10 fields as the farmer is more likely to find valid trends among 10 fields than two. Thanks for watching. And see you next time.

IN CONTEXT

Suppose you are a farmer and you want to try a new fertilizer in your field. One thing you could do is choose ten fields with similar soil nutrients, sunlight, and water--all variables that could affect the crop growth.

You could then apply the old fertilizer to five fields and the new fertilizer to the other five. By keeping all the other variables--soil nutrients, sunlight, water--consistent, the differences between the fields can be isolated and attributed to the old fertilizer or the new fertilizer.

Does the new fertilizer work? Is it effective? This is the idea behind controlling for all of these other variables.
term to know

Control
The principle of experimental design that requires that other variables which may confound the experiment be held constant between the treatment groups so that any differences in the groups can be attributed to the different treatments.

1b. Randomization
The second big idea of experimental design is randomization. The treatments must be assigned to the subject using a random process, otherwise known as "randomization." The purpose of random assignment is to try and filter out all the other sources of variation that you couldn't anticipate to control for.

EXAMPLE

Referring to the farmer example, even though you made the fields as similar as possible with respect to water, sunlight, and soil, it's possible that there is a variable that you didn't think to control for. Perhaps some fields had moles under the ground, and that would affect how the crops grow. How would you know to control for moles?

By randomly assigning treatments to the fields, you can hopefully get some fields with moles in fields with both the new and old fertilizer. Randomization smooths out those effects that other variables might bring into the equation.

hint
Randomizing also helps avoid bias, because you can’t be tempted to assign treatments to the experimental units you think might give favorable outcomes.

Randomization in an experiment does not really achieve the same purpose as a random selection in a sample. When you do a simple random sample, the idea is to get a sample that's representative of the population. In an experiment, the purpose of randomly assigning individuals to groups is to filter out unknown sources of variation. The assignment in an experiment, however, is fairly similar to the way you would randomly select in a sample.

term to know

Randomization
The principle of experimental design that requires that the subjects/experimental units be assigned to groups using some random process. This ensures that the two groups are roughly equal prior to assigning treatments.

1c. Replication
Replication is the last key idea in experimental design, which basically states that a bigger sample is better. Repeating the experiment on multiple subjects or experimental units is a better idea than doing a few. Why is that?

A larger size of the experiment means it's more likely that you can find trends that perhaps you wouldn't have found in a smaller experiment. The more you replicate, and the more experimental units you can get into your experiment, the more likely it is that you're going to find the true trends that arise, rather than some freak anomaly.

think about it
What if the farmer could have just found two fields that were similar to each other, instead of 10 fields, and randomly assigned one to get the new fertilizer and one to get the old. Isn't it possible in that case that maybe the field with the old fertilizer does very well just by random chance?

This would make it seem like the new fertilizer is not effective when perhaps it is. Or the opposite could happen, where it seems like the fertilizer is effective when it's not. It would be better to randomly assign five plots, as opposed to just two, as it is more likely that the farmer is going to find trends among those five plots that are more valid.

term to know

Replication
Repeating the experiment on multiple subjects/experimental units. This principle of experimental design that states that a larger experiment with more subjects/experimental units will allow us to more clearly see differences between the treatments.

summary
The components of an experimental design--that is, a well-designed experiment--are control, randomization, and replication. Control helps to isolate the effects of the treatments, randomization helps to make the groups as similar as possible and helps to avoid bias, and replication helps you to see the differences that might not have been evident if you had used a small sample. Treatments, again, are the things that the researchers administer to the subjects or experimental units.

Good luck!

Terms to Know
Control

The principle of experimental design that requires that other variables which may confound the experiment be held constant between the treatment groups, so that any differences in the groups can be attributed to the different treatments.

Experimental Design

The way in which an experiment is carried out. A good design has key elements of randomization, replication, and control.

Randomization

The principle of experimental design that requires that the subjects/experimental units be assigned to groups using some random process. This ensures that the two groups are roughly equal prior to assigning treatments.

Replication

Repeating the experiment on multiple subjects/experimental units. This principle of experimental design that states that a larger experiment with more subjects/experimental units will allow us to more clearly see differences between the treatments.

Treatment

Something the researchers administer to the subjects or experimental units.