Ch 9: Sampling Methods for Statistics

About This Chapter

Use the lessons in this chapter to learn more about statistical sampling. You can study for an exam, prepare for class or simply gain a better grasp of how statistics work.

Sampling Methods For Statistics - Chapter Summary

This chapter offers a flexible way to learn about the various sampling methods in statistics. The lessons explain the difference between population and sample size, as well as how random sampling works. Use the interactive quizzes included with each lesson to quickly identify any areas you need to work on. If you need to review a specific part of a video lesson, the video tags in the Timeline let you jump back to any point. You can study whenever you like, using your phone, tablet or computer. These lessons will ensure you are able to:

  • Define and share examples of sampling distributions and the central limit theorem
  • Use the central limit theorem to find probabilities
  • Share examples of simple random samples
  • Discuss characteristics of stratified random samples
  • Explain the meaning of cluster random samples
  • List advantages of systematic random samples
  • Describe issues in non-probability and probability sampling
  • Detail how the central limit theorem is used in business

10 Lessons in Chapter 9: Sampling Methods for Statistics
Test your knowledge with a 30-question chapter practice test
Difference between Populations & Samples in Statistics

1. Difference between Populations & Samples in Statistics

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

Issues in Probability & Non-Probability Sampling

2. Issues in Probability & Non-Probability Sampling

Choosing a sample is an important part of research. The two methods of sampling both come with their own set of issues. In this lesson, we'll look at the issues with probability and non-probability sampling.

What is Random Sampling? - Definition, Conditions & Measures

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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.

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.

Using the Central Limit Theorem in Business

10. Using the Central Limit Theorem in Business

The central limit theorem can be used to help evaluate data from various distribution patterns. Using this theorem we can apply statistical methods that would otherwise only apply to normal distributions of data.

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

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