GACE Math: Sampling - Videos & Lessons

About This Chapter

Use our lessons and quizzes to refresh your knowledge of various types of sampling and finding errors in sample distribution. This information may help you with questions on sampling when you take the GACE Math exam.

GACE Math: Sampling - Chapter Summary

Use these lessons and quizzes to brush up on types of samples, formulas for them and the central limit theorem. Our videos equip you to successfully answer questions on the following when you take the GACE Math exam:

Defining random sampling
Working with simple, stratified, cluster and systematic random samples
Understanding and using the central limit theorem
Finding the standard error and mean of the sampling distribution

These videos can help you re-learn topics you may not have encountered since high school as you prepare for the GACE Math test. You can study whenever you have a few minutes and complete the chapter as your schedule permits.

8 Lessons in Chapter 55: GACE Math: Sampling

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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.

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!

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.

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.

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.

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.

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.

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.

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Visit the GACE Mathematics (522): Practice & Study Guide page to learn more.

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