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Ch 56: PLACE Mathematics: Sampling

About This Chapter

Check out the video lessons and quizzes in this chapter to master the concepts you'll need to know for the PLACE Mathematics exam. This chapter helps you review concepts related to sampling.

PLACE Mathematics: Sampling - Chapter Summary

The lessons in this chapter cover topics like the benefits and risks of sampling and how to use sampling for research purposes. Our video lessons are short and can help refresh your memory about the different types of sampling. Some of the topics covered in this chapter include:

  • Conditions and measures of random sampling
  • Simple and stratified random samples
  • Cluster random sampling
  • Advantages of systematic random samples
  • Sampling distributions and the central limit theorem
  • Finding the mean and standard error of the sampling distribution
  • The central limit theorem and calculating probabilities about means

Our experienced instructors walk you through each lesson and can provide methods for remembering how to solve sampling problems. You can also practice answering exam-type questions by using our multiple-choice quizzes that are at the end of each lesson.

Objectives of the PLACE Mathematics: Sampling Chapter

This state test analyzes your understanding and application of the different ways to gather data. The Sampling chapter is part of the Statistics and Probability section of the exam, which makes up approximately 19% of the total test. All questions are multiple-choice and ask you to pick the correct answer.

8 Lessons in Chapter 56: PLACE Mathematics: 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

Other chapters within the PLACE Mathematics: Practice & Study Guide course

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