About This Chapter
Sampling Methods For Statistics - Chapter Summary
This chapter offers a flexible way to learn about the various sampling methods in statistics. The lessons explain the difference between population and sample size, as well as how random sampling works. Use the interactive quizzes included with each lesson to quickly identify any areas you need to work on. If you need to review a specific part of a video lesson, the video tags in the Timeline let you jump back to any point. You can study whenever you like, using your phone, tablet or computer. These lessons will ensure you are able to:
- Define and share examples of sampling distributions and the central limit theorem
- Use the central limit theorem to find probabilities
- Share examples of simple random samples
- Discuss characteristics of stratified random samples
- Explain the meaning of cluster random samples
- List advantages of systematic random samples
- Describe issues in non-probability and probability sampling
- Detail how the central limit theorem is used in business
1. Difference between Populations & Samples in Statistics
Before you start collecting any information, it is important to understand the differences between population and samples. This lesson will show you how!
2. Issues in Probability & Non-Probability Sampling
Choosing a sample is an important part of research. The two methods of sampling both come with their own set of issues. In this lesson, we'll look at the issues with probability and non-probability sampling.
3. 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.
4. 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!
5. 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.
6. 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.
7. 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.
8. 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.
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.
10. Using the Central Limit Theorem in Business
The central limit theorem can be used to help evaluate data from various distribution patterns. Using this theorem we can apply statistical methods that would otherwise only apply to normal distributions of data.
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Other chapters within the Business 212: Business Statistics course
- Introduction to Business Statistics
- Introduction to Categorical Data
- Descriptive Statistics: Measurement
- Descriptive Statistics: Representation
- Measures of Dispersion in Business
- Measures of Association & Correlation in Business
- Probability for Business Statistics
- Probability Distributions for Business Statistics
- Confidence Intervals
- Hypothesis Testing in Business
- Analysis of Variance
- Nonparametric Methods in Statistics
- Regression Analysis
- Understanding Business Forecasting
- Using Control Charts in Business