About This Chapter
Sampling Methods in Statistics - Chapter Summary and Learning Objectives
Watch this chapter's illustrated video lessons to get some help identifying the advantages and disadvantages of various population sampling methods - including simple, cluster and systematic random sampling techniques - as well as when and how to use them. Lessons in this chapter can teach you about the following:
- Random sampling methods
- Convenience sampling techniques
- The law of large numbers
|Simple Random Samples: Definition & Examples||Explore the uses of this data collection method.|
|What Is Random Sampling? - Definition, Conditions & Measures||Discover the benefits and drawbacks of selecting random samples.|
|Convenience Sampling: Definition & Limitations||Identify the shortcomings of this sampling method.|
|Stratified Random Samples: Definition, Characteristics & Examples||Study stratified random sampling methods and the ideal conditions for their use.|
|Cluster Random Samples: Definition, Selection & Examples||Learn what sets cluster sampling apart from stratified random sampling.|
|Systematic Random Samples: Definition, Formula & Advantages||Explore the processes involved in selecting a systematic random sample.|
|Understanding the Law of Large Numbers||Find out how the law of large numbers applies to statistics.|
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. Convenience Sampling in Statistics: Definition & Limitations
Convenience sampling is one of the most common types of sampling in research. This is because of the benefits that convenience sample brings to the researcher. However, there are some limitations. You will learn about both in this lesson.
4. 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.
5. 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.
6. 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.
7. 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.
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Other chapters within the DSST Principles of Statistics: Study Guide & Test Prep course
- Data Types & Measurements in Statistics
- Descriptive Statistics of Data Sets
- Visual Representations in Statistics
- Probability: Rules for Events
- Probability Combinations, Permutations & Expected Values
- Probability: Discrete & Continuous Distributions
- Correlation & Regression in Statistics
- Sampling Distributions in Statistics
- Hypothesis Testing in Inferential Statistics
- About the DSST Tests