About This Chapter
Below is a sample breakdown of the Sampling chapter into a 5-day school week. Based on the pace of your course, you may need to adapt the lesson plan to fit your needs.
|Day||Topics||Key Terms and Concepts Covered|
|Monday||Random sampling||Definition and random sampling, conditions that make a sample random|
|Tuesday||Simple random samples|
Stratified random samples
|Definition, characteristics and examples of simple and stratified random samples, information on how they can be selected or recognized|
|Wednesday|| Cluster random samples|
Systematic random samples
|Ways to select cluster random samples, with examples;|
Definition, formula and benefits of systematic random samples
|Thursday|| Law of large numbers|
|Overview of the law of large numbers and its applications;|
Definition of sampling distribution, overview of the central limit theorem
|Friday|| Mean and standard error|
Probabilities about means
|How to determine the mean and standard error of the sampling distribution;|
How to use normal distributions and the central limit theorem to find probabilities about means
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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