Statistical Inference for Everyone

Approaching an introductory statistical inference textbook in a novel way, this book is motivated by the perspective of "probability theory as logic". Targeted to the typical "Statistics 101" college student this book covers the topics typically treated in such a course - but from a fresh angle. This book walks through a simple introduction to probability, and then applies those principles to all problems of inference. Topics include hypothesis testing, data visualization, parameter inference, and model comparison. Statistical Inference for Everyone is freely available under the Creative Commons License, and includes a software library in Python for making calculations and visualizations straightforward.

Brian Blais is a Professor of Science and Technology at Bryant University and a research professor at the Institute for Brain and Neural Systems, Brown University. With a Ph.D. in Physics from Brown University, he has taught and published in such diverse areas as computational neuroscience, robotics, epidemic modeling, and environmental resource dynamics. He maintains his blog, bblais on the web where he explores the intersection of science and society.