About This Chapter
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.
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.
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!
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. 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!
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.
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.
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.
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.
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?
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.
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Other chapters within the MTLE Middle Level Mathematics: Practice & Study Guide course
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- 6th-8th Grade Math: Properties of Numbers
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- Understanding Square Roots & Radical Expressions
- Radical Expressions & Equations
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- Basic Algebraic Expressions & Factoring
- Algebraic Distribution
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- 6th-8th Grade Algebra: Writing & Solving Two-Step Equations
- 6th-8th Grade Algebra: Simplifying & Solving Rational Expressions
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