About This Chapter
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.
|Day||Topics||Key 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
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
|Thursday||Convenience 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
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.
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!
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.
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.
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.
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.
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!
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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Other chapters within the Statistics 101 Syllabus Resource & Lesson Plans course