Ch 1: Overview of Statistics Lesson Plans

About This Chapter

The Overview of Statistics chapter of this course is designed to help you plan and teach the fundamentals of gathering statistics in your classroom. The video lessons, quizzes and transcripts can easily be adapted to provide your lesson plans with engaging and dynamic educational content. Make planning your course easier by using our syllabus as a guide.

Weekly Syllabus

Below is a sample breakdown of the Overview of Statistics chapter into a 5-day school week. Based on the pace of your course, you may need to adapt the lesson plan to fit your needs.

DayTopicsKey Terms and Concepts Covered
Monday Descriptive and inferential statistics
Populations and samples
Parameters and statistics
Estimating a parameter
How descriptive and inferential statistics differ;
Definition of populations and samples and how they differ;
How parameters and statistics differ;
How to use statistics to estimate parameters, with examples
TuesdayQuantitative data
Categorical data
Discrete and continuous data
Nominal, ordinal, interval and ratio measurements
Definition and examples of quantitative data;
Definition and examples of categorical data;
Definitions of discrete and continuous data, how they differ;
Definition of nominal, ordinal, interval and ratio measurements, with examples
Wednesday Measuring the strength of a model through gathered data
Experiments and observational studies
Random selection and random allocation
How to prove a model through the use of data;
Definition and differences of experiments and observational studies;
How random allocation and random selection differ, advantages of each
ThursdayConvenience sampling in statistics
Design of randomized experiments
Analyzing and interpreting results from randomized experiments
Definition of convenience samples and its limitations;
Characteristics of a sound experiments;
How to interpret data from randomized experiments
Friday Confounding and bias in statistics
Bias in polls and surveys
Misleading use of statistics
Definition and examples of bias and confounding;
Definition of bias in polls and surveys, how to spot it, with examples;
Ways that statistics can be used to mislead

17 Lessons in Chapter 1: Overview of Statistics Lesson Plans
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.

How Randomized Experiments Are Designed

13. How Randomized Experiments Are Designed

When reading research or when conducting your own, it is important to understand the basic concepts of randomized experimental design that are covered in this lesson.

Analyzing & Interpreting the Results of Randomized Experiments

14. 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 & Bias in Statistics: Definition & Examples

15. 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

16. 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.

Misleading Uses of Statistics

17. Misleading Uses of Statistics

It can be too easy to present statistics in a way that is misleading. This lesson will cover the ways in which a statistic can be misleading and how to avoid and identify misleading statistics.

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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