Ch 7: Sampling Lesson Plans

About This Chapter

The Sampling chapter of this course is designed to help you plan and teach the types of samples and methods of sampling in your classroom. The video lessons, quizzes and transcripts can easily be adapted to provide your lesson plans with engaging and dynamic educational content. Make planning your course easier by using our syllabus as a guide.

Weekly Syllabus

Below is a sample breakdown of the Sampling chapter into a 5-day school week. Based on the pace of your course, you may need to adapt the lesson plan to fit your needs.

DayTopicsKey Terms and Concepts Covered
Monday Random samplingDefinition and random sampling, conditions that make a sample random
TuesdaySimple random samples
Stratified random samples
Definition, characteristics and examples of simple and stratified random samples, information on how they can be selected or recognized
Wednesday Cluster random samples
Systematic random samples
Ways to select cluster random samples, with examples;
Definition, formula and benefits of systematic random samples
Thursday Law of large numbers
Sampling distributions
Overview of the law of large numbers and its applications;
Definition of sampling distribution, overview of the central limit theorem
Friday Mean and standard error
Probabilities about means
How to determine the mean and standard error of the sampling distribution;
How to use normal distributions and the central limit theorem to find probabilities about means

9 Lessons in Chapter 7: Sampling Lesson Plans
Simple Random Samples: Definition & Examples

1. Simple Random Samples: Definition & Examples

Simple random sampling is a common method used to collect data in many different fields. From psychology to economics, simple random sampling can be the most feasible way to get information. Learn all about it in this lesson!

What is Random Sampling? - Definition, Conditions & Measures

2. What is Random Sampling? - Definition, Conditions & Measures

Random sampling is used in many research scenarios. In this lesson, you will learn how to use random sampling and find out the benefits and risks of using random samples.

Stratified Random Samples: Definition, Characteristics & Examples

3. Stratified Random Samples: Definition, Characteristics & Examples

Random sampling isn't always simple! There are many different types of sampling. In this lesson, you will learn how to use stratified random sampling and when it is most appropriate to use it.

Cluster Random Samples: Definition, Selection & Examples

4. Cluster Random Samples: Definition, Selection & Examples

Cluster random sampling is one of many ways you can collect data. Sometimes it can be confusing knowing which way is best. This lesson explains cluster random sampling, how to use it, and the differences between cluster and stratified sampling.

Systematic Random Samples: Definition, Formula & Advantages

5. Systematic Random Samples: Definition, Formula & Advantages

Systematic random sampling is a great way to randomly collect data on a population without the hassle of putting names in a bag or using a random number generator. In this lesson, learn all about how and when to use systematic random sampling.

Understanding the Law of Large Numbers

6. Understanding the Law of Large Numbers

The law of large numbers is a concept that is often misunderstood in statistics. In this lesson, you will learn the real meaning of the law of large numbers and how it is employed.

Sampling Distributions & the Central Limit Theorem: Definition, Formula & Examples

7. Sampling Distributions & the Central Limit Theorem: Definition, Formula & Examples

Want proof that all of this normal distribution talk actually makes sense? Then you've come to the right place. In this lesson, we look at sampling distributions and the idea of the Central Limit Theorem, a basic component of statistics.

Find the Mean & Standard Error of the Sampling Distribution

8. Find the Mean & Standard Error of the Sampling Distribution

Have you ever had a situation where one grade destroyed your average? Wouldn't you like a way of proving that your work was actually pretty good with that one exception? The standard error gives you such a chance.

Finding Probabilities About Means Using the Central Limit Theorem

9. Finding Probabilities About Means Using the Central Limit Theorem

The central limit theorem provides us with a very powerful approach for solving problems involving large amount of data. In this lesson, we'll explore how this is done as well as conditions that make this theorem valid.

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