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Ch 33: FTCE Math: Overview of Statistics

About This Chapter

Use this chapter to review the foundations of statistics. There are short, user-friendly videos, text lessons and self-assessment quizzes to guide your studies and help you get ready to take the FTCE Math exam.

FTCE Math: Overview of Statistics- Chapter Summary

Within this chapter, we have prepared specific text and video lessons related to statistics to aid in your study for the FTCE Math test. Review topics such as:

  • Descriptive and inferential statistics
  • Populations vs. samples
  • Difference between parameters and statistics
  • Quantitative data
  • Categorical data
  • Nominal, ordinal, interval and ratio measurements
  • Experiments vs. observational studies
  • Random selection and allocation
  • Convenience sampling
  • Results of randomized experiments
  • Confounding variables
  • Reliability and validity
  • Statistical significance

These text and video lessons are informative and short, so that you can make the most out of your study time. If you need to focus on areas that you have not mastered yet, you can use the timeline links below the video lessons. This will allow you to jump around the lesson to focus on areas you need to work on more.

13 Lessons in Chapter 33: FTCE Math: Overview of Statistics
Test your knowledge with a 30-question chapter practice test
Descriptive & Inferential Statistics: Definition, Differences & Examples

1. Descriptive & Inferential Statistics: Definition, Differences & Examples

Descriptive and inferential statistics each give different insights into the nature of the data gathered. One alone cannot give the whole picture. Together, they provide a powerful tool for both description and prediction.

Difference between Populations & Samples in Statistics

2. 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

3. 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.

What is Quantitative Data? - Definition & Examples

4. What is Quantitative Data? - Definition & Examples

Watch this video lesson to find out the difference between saying you have seven apples and saying that those apples are delicious. You will learn about quantitative data and why it is useful.

What is Categorical Data? - Definition & Examples

5. What is Categorical Data? - Definition & Examples

Categorical data is one of two types of data that you can collect when conducting research. This lesson will teach you how to understand and use categorical data.

Nominal, Ordinal, Interval & Ratio Measurements: Definition & Examples

6. Nominal, Ordinal, Interval & Ratio Measurements: Definition & Examples

Different types of data can be grouped and measured in different ways. In this lesson, you will learn about nominal, ordinal, interval, and ratio measurements.

Experiments vs Observational Studies: Definition, Differences & Examples

7. 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.

Random Selection & Random Allocation: Differences, Benefits & Examples

8. Random Selection & Random Allocation: Differences, Benefits & Examples

Random selection and random allocation are often confused with one another. This lesson will help you remember the differences between them and learn how to use each method.

Convenience Sampling in Statistics: Definition & Limitations

9. 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.

Analyzing & Interpreting the Results of Randomized Experiments

10. Analyzing & Interpreting the Results of Randomized Experiments

Analyzing and interpreting the results of an experiment can be a confusing process, and it's easy to make mistakes. This lesson will help you understand the important factors of experiment analysis.

Confounding Variables in Statistics: Definition & Examples

11. Confounding Variables in Statistics: Definition & Examples

Statistics can be a powerful tool in helping researchers understand and solve problems. However, there are faults that can occur in statistical research. In this lesson, you will learn about one of these faults: confounding variables.

Reliability & Validity in Psychology: Definitions & Differences

12. Reliability & Validity in Psychology: Definitions & Differences

How do validity and reliability contribute to study design in psychology? In this lesson, you'll look at how experiments can fail reliability and validity requirements to get an idea of the challenges behind conducting significant psychological research.

Statistical Significance: Definition, Levels & Critical Regions

13. Statistical Significance: Definition, Levels & Critical Regions

This lesson explores the basic principle of statistical significance and why it is important to understand when performing nearly any statistical test.

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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Other Chapters

Other chapters within the FTCE Mathematics 6-12 (026): Practice & Study Guide course

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