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Random Selection & Random Allocation: Differences, Benefits & Examples

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  • 0:05 Random Selection
  • 1:53 Random Allocation
  • 3:27 Differences and Uses
  • 5:00 Lesson Summary
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Lesson Transcript
Instructor: Chelsea Schuyler
Random selection and random allocation are often confused with one another. This lesson will help you remember the differences between them and learn how to use each method.

Random Selection

Aubree is conducting an experiment. She wants to find out if oranges consumed on a regular basis will help improve the chances of someone staying healthy during the winter months. Aubree will have to design a research experiment to find the answer to this question. As she is designing her research, she will need to understand random selection and random allocation.

In this lesson, you will learn about random selection and random allocation, how to use them and the differences between the two. First, let's discuss random selection.

Random selection is the method of selecting a sample from the population to participate in a study. Basically, random selection is the way Aubree will choose who will be a part of her study. Most studies use some sort of random sampling to select participants. There are different ways you can select participants for a study.

First, Aubree will need to decide on her population. A population is all members of a specified group. For example, let's say that Aubree wants to study the effects of oranges on college students. This means that her population will be all college students in the world. Of course, it's hard to conduct a study of this size. We can solve this problem using random selection. Aubree can get a sample of her population by selecting students at a local college for her study.

A sample is a part of a population used to describe the whole group. For example, Aubree can conduct her study with a random selection of students in certain classes at the college, or she can select every other student that is willing to be a part of the study.

There are many different ways you can get a sample from your population. These include random sampling, simple random sampling, cluster sampling, stratified sampling, and systematic sampling. Now that we have covered random selection, let's move on to random allocation.

Random Allocation

Remember, Aubree is studying the effects of orange consumption on college students. She wants to know if consuming oranges on a regular basis will help improve the chances of someone staying healthy during the winter months.

As such, Aubree has to compare two groups of students: those that consume oranges on a regular basis and those that consume oranges on an irregular basis or not at all. Aubree will have to use a control group and a treatment group because she wants to see the effects of orange consumption and will need to compare two groups.

The control group is the group that remains untreated throughout the duration of an experiment. For example, in Aubree's experiment the students that do not consume oranges on a regular basis would be the control group.

The treatment is the variable in an experiment that is used on an experimental group. For example, the oranges in Aubree's experiment would be the treatment, and the experimental group would be whomever is selected to receive the treatment. This is where random allocation comes in.

Random allocation is the method used to select members of a sample to receive the treatment in an experiment. Random allocation is the way Aubree will select her experimental group, or the group that will consume oranges in the experiment. She can select this group using similar methods that she used with random sampling. For example, she can write all of the participants' names on a piece of paper and randomly select half of the names from a hat. The names selected would be the experimental group. Now let's discuss the differences and uses of random selection and random allocation.

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