Ch 25: HiSET Mathematics: Sampling

About This Chapter

Get ready for the High School Equivalency Test (HiSET) Mathematics exam with this chapter lesson on sampling. Our instructional videos and quizzes can help you make the most of your test preparation.

HiSET Mathematics: Sampling - Chapter Summary

Watch the engaging video lessons to get an understanding of how to analyze and interpret the demographics of particular samples. The videos and transcripts explain key terms and formulas you'll need to know to solve sampling problems on the HiSET Mathematics exam. The types of topics you'll see in this chapter include:

  • Conditions of random sampling
  • Using simple random samples
  • Differentiating between stratified random samples and cluster random samples
  • Measuring with systematic random samples

Each lesson is less than ten minutes long and explains the definitions and conditions of each sample in a practical way that makes this chapter fun to learn about. Our instructors are also available to answer any additional questions you may have.

Objectives of the HiSET Mathematics: Sampling Chapter

The HiSET Math exam is designed to measure your ability to solve quantitative problems using reasoning skills and basic concepts. The topics in this Sampling chapter are part of the Data Analysis/Probability Statistics portion of the exam, which makes up 18% of the entire math test.

There are 50 total questions on the math exam and all are multiple-choice, asking you to select one correct answer. Our lesson quizzes are also multiple-choice and can help you test your understanding of sampling concepts as well as give you practice answering exam-type questions.

5 Lessons in Chapter 25: HiSET Mathematics: Sampling
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.

Chapter Practice Exam
Test your knowledge of this chapter with a 30 question practice chapter exam.
Not Taken
Practice Final Exam
Test your knowledge of the entire course with a 50 question practice final exam.
Not Taken

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