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

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Lesson Transcript

Instructor:
*Cathryn Jackson*

Cat has taught a variety of subjects, including communications, mathematics, and technology. Cat has a master's degree in education and is currently working on her Ph.D.

Before you start collecting any information, it is important to understand the differences between population and samples. This lesson will show you how!

Jasmine is collecting information for the student council at her school. The student council organization wants to know the student's preferences for cafeteria meals. When collecting data or doing an experiment like Jasmine, you will have to understand the difference between populations and samples.

In this lesson, we will discuss populations and samples and how they are used in data collection and statistics.

A **population** is all members of a specified group. For example, if Jasmine wants to collect information about the students in her school, then the population would be all of the students in the school. The school would be the group in this particular scenario, and the members would be the students. Therefore, the population would be all of the students in Jasmine's school. However, most of the time it isn't practical to get information from every member of a population. When this happens, we have to find a different way of getting information that represents the population without actually asking the entire population. Jasmine has the same issue, she won't be able to ask every student in the school what his or her preference is when it comes to cafeteria meals. Instead, Jasmine will need to get a sample.

A **sample** is a part of a population used to describe the whole group. Jasmine can get a select group of students from her school as a population for her sample. This way she can get an idea of the preferences of the students without asking every student in the school. In order to get a sample, Jasmine can only use students that are from the school. She can't ask students that are from another school or aren't students because they aren't part of the desired population. 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. You'll learn more about each of these types of sampling in future lessons!

Also, populations and samples do not have to be people. They can be a deck of cards, dice, animals, books, pretty much anything that you might be collecting data.

So let's practice identifying populations and samples:

Jasmine is now in charge of purchasing new jackets for the athletics teams at her school. She surveys some people on the basketball team and some students that aren't involved in athletics. Based on the information given, can you identify the population and the sample? Pause the video here if you need more time.

What do you think? In this scenario, the population was all of the members of all of the athletics teams. The sample would be the members of the basketball team that Jasmine surveyed. The students that aren't involved in athletics cannot be included in this sample because they aren't members of the population.

Population and samples are important elements of collecting and analyzing data. Remember, a **population** is all members of a specified group. This could be all members of an institution, like a school in Jasmine's case, or even all members of a town, or a specific interest group. A **sample** is a part of a population used to describe the whole group. Therefore, if your population was the citizens that lived in a town, the sample would be select citizens that lived in that town. Don't forget! Something cannot be a part of a sample if it isn't already part of the population!

Make it your goal to achieve these objectives after the lesson ends:

- Characterize statistical populations and samples
- Provide examples of both

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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
- 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
- Convenience Sampling in Statistics: Definition & Limitations 6:27
- 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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