About This Chapter
Sampling - Chapter Summary and Learning Objectives
In this chapter's video lessons, instructors illustrate the use of sampling to get a representative measure of an entire population. Let them introduce you to the different types of sampling methods and show you how to find a sampling distribution's mean and standard error. By the time you finish this chapter, you should be familiar with the following:
- Simple, stratified, cluster and systematic random samples
- The law of large numbers and central limit theorem
- Sampling distribution, standard error and the sample mean
|What is Random Sampling? - Definition, Conditions & Measures||Understand what conditions must be met for a sample to be random.|
|Simple Random Samples: Definition & Examples||Learn how to select or recognize a simple random sample.|
|Stratified Random Samples: Definition, Characteristics & Examples||Explain methods for selecting or recognizing a stratified random sample.|
|Cluster Random Samples: Definition, Selection & Examples||Find out how to select or recognize a cluster random sample.|
|Systematic Random Samples: Definition, Formula & Advantages||Understand the steps involved in selecting or recognizing a systematic random sample.|
|Understanding the Law of Large Numbers||Examine the law of large numbers as it relates to probability theory.|
|Sampling Distributions & the Central Limit Theorem: Definition, Formula & Examples||Discover what a sampling distribution is and explore the central limit theorem.|
|Find the Mean & Standard Error of the Sampling Distribution||Determine the mean and standard error for the sampling distribution of the sample mean.|
|Finding Probabilities About Means Using the Central Limit Theorem||Find probabilities for a sample mean using normal distributions and the central limit theorem.|
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!
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.
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. 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.
7. 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.
8. 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.
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.
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