OpenIntro Statistics offers a traditional introduction to statistics at the college level. This textbook is widely used at the college level and offers an exceptional and accessible introduction for students from community colleges to the Ivy League. The textbook has been thoroughly vetted with an estimated 20,000 students using it annually.
Introductory Statistics by Various Authors (2020): OpenStax.
Introductory Statistics follows scope and sequence requirements of a one-semester introduction to statistics course and is geared toward students majoring in fields other than math or engineering. The text assumes some knowledge of intermediate algebra and focuses on statistics application over theory. Introductory Statistics includes innovative practical applications that make the text relevant and accessible, as well as collaborative exercises, technology integration problems, and statistics labs.
Introductory Statistics with Randomization and Simulation by David Diez (2014): OpenIntro.
Bringing a fresh approach to intro statistics, ISRS introduces inference faster using randomization and simulation techniques.
Answering questions with data: Introductory Statistics for Psychology Students by M.J. Crump (2019): CrumpLab.
This is a free textbook teaching introductory statistics for undergraduates in Psychology. This textbook is part of a larger OER course package for teaching undergraduate statistics in Psychology, including this textbook, a lab manual, and a course website. All of the materials are free and copiable, with source code maintained in Github repositories.
Learning statistics with R: A tutorial for psychology students and other beginners by Danielle Navarro (2019): LaTex.
Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. The book discusses how to get started in R as well as giving an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.
Think Stats: Probability and Statistics for Programmers by Allen B. Downey (2014): Green Tea Press.
Think Stats is an introduction to Probability and Statistics for Python programmers. Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets.
Introductory Business Statistics by Thomas K. Tiemann (2010): BCcampus.
The book "Introductory Business Statistics" by Thomas K. Tiemann explores the basic ideas behind statistics, such as populations, samples, the difference between data and information, and most importantly sampling distributions. The author covers topics including descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics. Using real-world examples throughout the text, the author hopes to help students understand how statistics works, not just how to "get the right number."
Concepts in Statistics (Lumen)
We organized this course around the Big Picture of Statistics. As we learn new material, we will always look at how these new ideas relate to the Big Picture. In this way the Big Picture is a diagram that will help us organize and understand the material we will learn throughout the course.
Introduction to Statistics (Saylor)
The purpose of this course is to introduce you to the subject of statistics as a science of data. There is data abound in this information age; how to extract useful knowledge and gain a sound understanding in complex data sets has been more of a challenge. In this course, we will focus on the fundamentals of statistics, which may be broadly described as the techniques to collect, clarify, summarize, organize, analyze, and interpret numerical information.
NYU Statistics for Behavioral Sciences (YouTube)
Series of 23 educational videos that walk students through understanding statistics used for behavioral science and psychology courses. Presented on YouTube by New York University.
Probabilistic Systems Analysis and Applied Probability
Welcome to 6.041/6.431, a subject on the modeling and analysis of random phenomena and processes, including the basics of statistical inference. Nowadays, there is broad consensus that the ability to think probabilistically is a fundamental component of scientific literacy.
Introduction to Statistics (OpenStax)
Mathematics & Statistics Books (Lyryx)
OpenIntro Statistics (Lumen)
Statistics Bookshelf (LibreTexts)
Statistics Using Technology (Lumen)