Ch 56: NMTA Math: Sampling

About This Chapter

New Mexico now uses the National Evaluation Series (NES) Math examination for teacher certification instead of the NMTA Math examination. Prepare to answer the examination's sampling questions by studying this chapter's online video lessons.

NMTA Math: Sampling - Chapter Summary

The video lessons in this chapter examine sampling as a method of gathering information on a number of things or people. Watch the tutorial lessons to prepare to answer sampling questions on the NMTA examination. Recall what you've studied about stratified random and simple random sampling research techniques, and perhaps learn a few new concepts along the way. This chapter might help you to:

  • Define and explain how to use simple random, cluster random, stratified random and systematic random sampling
  • Demonstrate the central limit theorem
  • Discuss the use of sampling distributions
  • Calculate probabilities about means

You'll find that these lessons are convenient because they allow you to brush up on sampling facts that you may have forgotten as you prepare to take the NMTA Math examination. You can learn interactively at any location by using your Web-connected technology. Ask questions of the math experts and use the corresponding written transcripts as an extra study resource. When you're ready, take the self-assessment quizzes to test your retention of the chapter's material.

NMTA Math: Sampling Chapter Objectives

Sampling techniques, such as those you've studied in this chapter, are covered in the Statistics, Probability and Discrete Mathematics section of the NMTA Math examination. The equivalent of 19% of the entire test score, this section will determine your ability to solve problems with sampling and to teach related principles and techniques to your students. There are 150 multiple-choice questions on the computer-based examination.

8 Lessons in Chapter 56: NMTA 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.
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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