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Ch 40: Michigan Merit Exam - Math: Data Collection & Analysis

About This Chapter

If you need help with the topic of collecting data and analyzing it for the math section of the Michigan Merit Exam, you've come to the correct place! Here you will find short and enjoyable video lessons and quizzes that will make your test preparation easier.

Michigan Merit Exam - Math: Data Collection & Analysis - Chapter Summary

In this chapter, you will find short and enjoyable videos that will help you recall the ways of distinguishing between statistics and parameters and different types of samples. End of the lesson quizzes will test your comprehension of the definition of observational studies. These study materials will prepare you for questions on the Michigan Merit Exam about the following:

  • Samples and populations
  • Statistics and parameters
  • Examples and meanings of bias and confounding
  • Sources, definitions, and samples of biases in surveys and polls
  • Simple random samples and stratified random samples
  • Cluster random samples and systematic random samples
  • Distinction between observational studies and experiments

Video tags allow you to easily jump ahead or go back in the video lessons. Video transcripts that contain bold printed words are also provided if you would like to read them. Do not hesitate to ask our instructors if you have questions.

10 Lessons in Chapter 40: Michigan Merit Exam - Math: Data Collection & Analysis
Test your knowledge with a 30-question chapter practice test
Difference between Populations & Samples in Statistics

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!

Defining the Difference between Parameters & Statistics

2. Defining the Difference between Parameters & Statistics

Using data to describe information can be tricky. The first step is knowing the difference between populations and samples, and then parameters and statistics.

Confounding & Bias in Statistics: Definition & Examples

3. Confounding & Bias in Statistics: Definition & Examples

Statistics can be a powerful tool in research. Unfortunately, statistics can also have faults. In this lesson, you will learn about the faults in statistics and how to critically examine research.

Bias in Polls & Surveys: Definition, Common Sources & Examples

4. Bias in Polls & Surveys: Definition, Common Sources & Examples

When Mark Twain commented that there were three types of lies, he included statistics in the count. In this lesson, we look at bias, one of the ways in which statistics can mislead, and in some cases, flat out lie to us.

What is Random Sampling? - Definition, Conditions & Measures

5. 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

6. 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

7. 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

8. 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

9. 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.

Experiments vs Observational Studies: Definition, Differences & Examples

10. Experiments vs Observational Studies: Definition, Differences & Examples

There are different ways to collect data for research. In this lesson, you will learn about collecting data through observational studies and experiments and the differences between each.

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.
Not Taken

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