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Ch 55: WEST Math: Sampling

About This Chapter

The quick and entertaining videos in this chapter provide a fun way to review the sampling concepts you'll likely encounter on the WEST Math certification exam. Companion transcripts make it easy to track down key terms and ideas.

WEST Math: Sampling - Chapter Summary

As you work through the engaging, accessible video lessons in this chapter, you will review the sampling concepts you need to know for the WEST Math test. Chapter topics include:

  • Different types of random samples
  • Sampling distributions and the central limit theorem
  • Methods for calculating mean and standard error
  • How to calculate probabilities about means

The self-assessment after each lesson allows you to gauge your mastery of the sampling concepts you've covered. If you need any clarification or get stuck, reach out to our instructors for assistance.

8 Lessons in Chapter 55: WEST Math: Sampling
Test your knowledge with a 30-question chapter practice test
What is Random Sampling? - Definition, Conditions & Measures

1. 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.

Simple Random Samples: Definition & Examples

2. 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!

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.

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

6. 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

7. 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

8. 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.

Chapter Practice Exam
Test your knowledge of this chapter with a 30 question practice chapter exam.
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Practice Final Exam
Test your knowledge of the entire course with a 50 question practice final exam.
Not Taken

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Other chapters within the NES Mathematics - WEST (304): Practice & Study Guide course

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