Letter to Instructors
We all know the issues with the price of higher education. One small, but still significant, aspect over which instructors may have some level of control is the materials used. The use of Open Educational Resources (OER) is a growing trend that I hope will continue to catch on.
Statistics is about separating the signal from the noise, deciphering what is actually significant versus what is just happening due to random chance. In addition to demonstrating the basic concepts needed to do that, this book attempts to focus on what is significant and eliminate some of the noise that may commonly be found in many introductory statistics texts. In the realm of introductory statistics there are many OER options available, the most complete being Introductory Statistics from OpenStax and OpenIntro Statistics. While these are both adequate options, the beauty of OER is that you can customize material to the needs of your course and students which is what I have tried to do here. Specific to this book I have “remixed” sections from the aforementioned texts and sprinkled in some thoughts of my own.
The intended audience for Significant Statistics: An Introduction to Statistics includes students who may not have completed a calculus prerequisite. In contrast to similar introductory statistics texts, this text has an emphasis on data collection, but does not deeply delve into probability topics. The text also takes a repetitive approach to one-sample inference to drive those basic concepts home. It includes introductory level material on two-sample inference (Ch 8) and omits advanced inference topics such as Chi Square or ANOVA which are infrequently covered in an introductory-level statistics course. The regression chapter (Ch 9) could be moved anywhere in the book by omitting the section on inference for regression (9.5). Please see the Attribution section for more details.
I’ve also tried to make this book technology agnostic in order to accommodate a wide variety of technology preferences. It is my belief that if one understands the concepts, they can figure out how to use any technology to accomplish the task at hand. However, if learners lean on technology when first being introduced to the concepts, true understanding may not be achieved.
I hope you will consider using this text!
John Morgan Russell
P.S: The Chapter Wrap-ups and Glossary functionalities are still a work in progress!
Instructors reviewing, adopting, or adapting this textbook please help us understand your use by filling out this form: https://bit.ly/stat-interest
Supplemental multimedia material aligned with this textbook including videos, audio-only versions of the videos in podcast format, and PowerPoint lecture notes whihc I have branded “Significant Statistics can be found at:
- Significant Statistics Website
- Significant Statistics YouTube Channel
- Significant Statistics Podcast Channel
Significant Statistics: An Introduction to Statistics is intended for the one-semester introduction to statistics course for students who are not mathematics or engineering majors. It focuses on the interpretation of statistical results, especially in real world settings, and assumes that students have an understanding of intermediate algebra. In addition to end of section practice and homework sets, examples of each topic are explained step-by-step throughout the text and followed by a Your Turn problem designed as extra practice for students.
Having successfully completed the course the student should be able to:
- Identify and critique the use of statistical reasoning in science, industry, and public discourse
- Identify appropriate data to be gathered to answer research questions
- Assign appropriate data collection methods
- Apply appropriate methods of data visualization to explore data from a variety of disciplines
- Analyze data provided and use relevant technology when needed
- Appropriately interpret results of data exploration and statistical tests
- Employ critical thinking to make decisions
- Apply ethical reasoning and principles to scientific research
Coverage and scope
Chapter 1 Sampling and Data
Chapter 2 Descriptive Statistics
Chapter 3 Probability Topics
Chapter 4 Discrete Random Variables
Chapter 5 Continuous Random Variables
Chapter 6 Introduction to Inference
Chapter 7 One Sample Inference
Chapter 8 Two Sample Inference
Chapter 9 Simple Linear Regression