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Statistics 101: Principles of Statistics11 chapters | 144 lessons | 9 flashcard sets

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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.

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.

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.

You can use random allocation, random selection, both, or neither in a study. Aubree's experiment used both methods. There are a few things to keep in mind with this. Random allocation is only used for experimental studies with a control and a treatment. If you only use this method, then you did not randomly select the sample of your study. For example, let's say that out of convenience, Aubree asks people that do not eat oranges to participate in her study. This would not be random selection because she isn't selecting members randomly out of her population, but she could still use random allocation to find the experimental and control groups.

If Aubree did not want to find the cause and effect of orange consumption but simply wanted to see if there is a correlation between people that consume oranges, those that don't consume oranges, and the person's overall health, then she could use random selection for her participants. This would be an observational study because she would only be collecting data from the participant's natural eating patterns, not experimenting with the effects of orange consumption. Observational studies only use random selection; they do not use random allocation.

Aubree could also potentially use neither random selection nor random allocation in her study. For example, she could ask a few of her college friends to participate in the study. Then, she could have the participants that like eating oranges be part of the experimental group and the participants that do not like eating oranges be part of the control group. This study uses neither random selection nor random allocation.

Both **random selection** and **random allocation** are important parts of research and collecting data. **Random selection** is the method of selecting a sample from the population to participate in a study. Basically, random selection is the way a researcher chooses the participants in a study. This requires the researcher to identify the population and the sample. A **population** is all members of a specified group. Once you know the population, you can use random selection to determine the sample. A **sample** is a part of a population used to describe the whole group.

**Random allocation** is the method used to select members of a sample to receive the treatment in an experiment. This is how a researcher will determine the control and the experimental group. The **control group** is the group that remains untreated throughout the duration of an experiment, and the **treatment** is the variable in an experiment that is used on an experimental group.

A study can have any combination of random allocation and random selection or have neither. Remember, random allocation is only used for experimental studies with a control and a treatment, and observational studies only use random selection.

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Statistics 101: Principles of Statistics11 chapters | 144 lessons | 9 flashcard sets

- Descriptive & Inferential Statistics: Definition, Differences & Examples 5:11
- Difference between Populations & Samples in Statistics 3:24
- Defining the Difference between Parameters & Statistics 5:18
- Estimating a Parameter from Sample Data: Process & Examples 7:46
- What is Quantitative Data? - Definition & Examples 4:11
- What is Categorical Data? - Definition & Examples 5:25
- Discrete & Continuous Data: Definition & Examples 3:32
- Nominal, Ordinal, Interval & Ratio Measurements: Definition & Examples 8:29
- The Purpose of Statistical Models 10:20
- Experiments vs Observational Studies: Definition, Differences & Examples 6:21
- Random Selection & Random Allocation: Differences, Benefits & Examples 6:13
- How Randomized Experiments Are Designed 8:21
- Analyzing & Interpreting the Results of Randomized Experiments 4:46
- Confounding & Bias in Statistics: Definition & Examples 3:59
- Confounding Variables in Statistics: Definition & Examples 5:20
- Bias in Statistics: Definition & Examples 7:24
- Bias in Polls & Surveys: Definition, Common Sources & Examples 4:36
- Misleading Uses of Statistics 8:14
- Go to Overview of Statistics

- Go to Probability

- Go to Sampling

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