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Ch 44: CEOE Middle Level/Intermediate Math: Overview of Statistics

About This Chapter

Let our instructors help you increase your confidence and competency as you prepare for the Certification Examinations for Oklahoma Educators (CEOE) Middle Level/Intermediate Math test. This chapter contains a complete overview of statistics.

CEOE Middle Level/Intermediate Math: Overview of Statistics - Chapter Summary

This chapter contains short video lessons geared to help the Oklahoma teacher or aspiring teacher get ready for taking the CEOE Middle Level/Intermediate Math test. You'll cover topics like descriptive and inferential statistics, estimating parameters with sample data, and the use of statistical models. You'll also find lessons on the following topics:

  • Populations versus samples in statistics
  • Differentiating between parameters and statistics
  • Quantitative, categorical, discrete and continuous data
  • Nominal, ordinal, interval and ratio measurements
  • How experiments differ from observational studies
  • Using random selection as opposed to random allocation
  • Uses and limitations of convenience sampling in statistics
  • Analyzing and interpreting the results of randomized experiments
  • Confounding variables and effect of bias in statistics

Lesson videos are hosted by our expert instructors and average 5-10 minutes in length. Each lesson comes with a full transcript for following along with the instructor, and ends with a short quiz on key topics and concepts. Going back over material is easy with our keyword-base timeline tool that can take you straight to any part of the video. Also, we include a feature that allows you to ask the instructor any question you might have about the lesson material.

15 Lessons in Chapter 44: CEOE Middle Level/Intermediate 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.

Estimating a Parameter from Sample Data: Process & Examples

4. Estimating a Parameter from Sample Data: Process & Examples

One of the most useful things we can do with data is use it to describe a population. Learn how in this lesson as we discuss the concepts of parameters and samples.

What is Quantitative Data? - Definition & Examples

5. 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

6. 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.

Discrete & Continuous Data: Definition & Examples

7. Discrete & Continuous Data: Definition & Examples

You might be surprised to find that data is more than just a collection of numbers. Data is divided into several categories, including discrete and continuous data. Find out why!

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

8. 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.

The Purpose of Statistical Models

9. The Purpose of Statistical Models

Understanding statistics requires that you understand statistical models. This lesson will help you understand the purpose of statistics, statistical models, and types of variables.

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.

Random Selection & Random Allocation: Differences, Benefits & Examples

11. 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

12. 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

13. 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

14. 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.

Bias in Statistics: Definition & Examples

15. 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 potential biases in research.

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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Other Chapters

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