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Ch 2: Sampling Methods in Statistics

About This Chapter

Study simple and stratified random sampling methods, convenience sampling, the law of large numbers and more with the help of this chapter's statistics video lessons. Multiple-choice quizzes are also included to check your understanding of these topics.

Sampling Methods in Statistics - Chapter Summary and Learning Objectives

Watch this chapter's illustrated video lessons to get some help identifying the advantages and disadvantages of various population sampling methods - including simple, cluster and systematic random sampling techniques - as well as when and how to use them. Lessons in this chapter can teach you about the following:

  • Random sampling methods
  • Convenience sampling techniques
  • The law of large numbers

Video Objective
Simple Random Samples: Definition & Examples Explore the uses of this data collection method.
What Is Random Sampling? - Definition, Conditions & Measures Discover the benefits and drawbacks of selecting random samples.
Convenience Sampling: Definition & Limitations Identify the shortcomings of this sampling method.
Stratified Random Samples: Definition, Characteristics & Examples Study stratified random sampling methods and the ideal conditions for their use.
Cluster Random Samples: Definition, Selection & Examples Learn what sets cluster sampling apart from stratified random sampling.
Systematic Random Samples: Definition, Formula & Advantages Explore the processes involved in selecting a systematic random sample.
Understanding the Law of Large Numbers Find out how the law of large numbers applies to statistics.

7 Lessons in Chapter 2: Sampling Methods in Statistics
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.

Convenience Sampling in Statistics: Definition & Limitations

3. Convenience Sampling in Statistics: Definition & Limitations

Convenience sampling is one of the most common types of sampling in research. This is because of the benefits that convenience sample brings to the researcher. However, there are some limitations. You will learn about both in this lesson.

Stratified Random Samples: Definition, Characteristics & Examples

4. 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

5. 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

6. 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

7. 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.

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