Ch 49: Statistics & Sampling Basics

About This Chapter

Watch the video lessons in this chapter to learn more about statistics and sampling. Review the basics of statistics and sampling from anywhere using our mobile-friendly lessons and quizzes.

Statistics & Sampling Basics - Chapter Summary

Throughout the lessons in this chapter, you will cover essential facts about statistics and sampling. This chapter will explain the vocabulary that is associated with this topic and share the calculations you will need to properly work with data sets.

The aim of this chapter is to help you work better with data sets and statistical information. Our instructors will teach you about different types of samples and their characteristics. Their engaging lessons will make studying easier by giving you examples of concepts spread in data sets that may be more difficult to understand. You will learn to define the center of a data set and the different ways you can calculate the center. After you've finished this chapter, you should be able to:

  • Identify simple, stratified, cluster and systematic random samples
  • Describe the law of large numbers
  • Find mean, median, mode and range
  • Use visual representations of data sets
  • Define spread in data sets

The short and engaging videos in this chapter will outline the basics of statistics and sampling. Get more acquainted with this topic through our video lessons and practice with our multiple-choice quizzes. Your own personal dashboard can help you keep track of your progress through the chapter.

13 Lessons in Chapter 49: Statistics & Sampling Basics
Test your knowledge with a 30-question chapter practice test
What is Random Sampling? - Definition, Conditions & Measures

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.

Simple Random Samples: Definition & Examples

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!

Stratified Random Samples: Definition, Characteristics & Examples

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.

Cluster Random Samples: Definition, Selection & Examples

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.

Systematic Random Samples: Definition, Formula & Advantages

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.

Understanding the Law of Large Numbers

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.

What is the Center in a Data Set? - Definition & Options

7. What is the Center in a Data Set? - Definition & Options

Finding the center in a data set can sometimes be a little confusing. This lesson will help you determine the correct method for finding the center in a data set, and when you are finished, test your knowledge with a short quiz!

Mean, Median & Mode: Measures of Central Tendency

8. Mean, Median & Mode: Measures of Central Tendency

By describing the data using central tendency, a researcher and reader can understand what the typical score looks like. In this lesson, we will explore in more detail these measures of central tendency and how they relate to samples and populations.

How to Calculate Mean, Median, Mode & Range

9. How to Calculate Mean, Median, Mode & Range

Measures of central tendency can provide valuable information about a set of data. In this lesson, explore how to calculate the mean, median, mode and range of any given data set.

Calculating the Mean, Median, Mode & Range: Practice Problems

10. Calculating the Mean, Median, Mode & Range: Practice Problems

Calculating the mean, median, mode, and range of a data set is a fundamental part of learning statistics. Use this video to practice your skills and then test your knowledge with a short quiz.

Visual Representations of a Data Set: Shape, Symmetry & Skewness

11. Visual Representations of a Data Set: Shape, Symmetry & Skewness

Visual representations are a fantastic way of understanding and analyzing your data. Use this lesson to understand the characteristics of visual representations of data.

The Mean vs the Median: Differences & Uses

12. The Mean vs the Median: Differences & Uses

Most people can find the mean and the median of a data set, but do you know when to use the mean and when to use the median to describe the information?

Spread in Data Sets: Definition & Example

13. Spread in Data Sets: Definition & Example

Identifying the spread in data sets is a very important part of statistics. You can do this several ways, but the most common methods are through range, interquartile range, and variance.

Chapter Practice Exam
Test your knowledge of this chapter with a 30 question practice chapter exam.
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Practice Final Exam
Test your knowledge of the entire course with a 50 question practice final exam.
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