{"id":20278,"date":"2026-01-30T07:35:33","date_gmt":"2026-01-30T07:35:33","guid":{"rendered":"https:\/\/www.booksofall.com\/in\/?post_type=product&p=20278"},"modified":"2026-01-30T07:35:33","modified_gmt":"2026-01-30T07:35:33","slug":"statistical-inference-for-data-science","status":"publish","type":"product","link":"https:\/\/www.booksofall.com\/in\/statistical-inference-for-data-science\/","title":{"rendered":"Statistical inference for data science"},"content":{"rendered":"

Introduction <\/b><\/h2>\n

Before beginning\u00a0<\/b><\/p>\n

This book is designed as a companion to the Statistical Inference Coursera<\/a> class as part of the Data Science Specialization<\/a>, a ten course program offered by three faculty, Jeff Leek, Roger Peng and Brian Caffo, at the Johns Hopkins University Department of Biostatistics.<\/p>\n

The videos associated with this book can be watched in full here<\/a>, though the relevant links to specific videos are placed at the appropriate locations throughout.<\/p>\n

Before beginning, we assume that you have a working knowledge of the R programming language. If not, there is a wonderful Coursera class by Roger Peng, that can be found here<\/a>.<\/p>\n

The entirety of the book is on GitHub here<\/a>. Please submit pull requests if you find errata! In addition the course notes can be found also on GitHub here<\/a>. While most code is in the book, all\u00a0<\/i>of the code for every figure and analysis in the book is in the R markdown files files (.Rmd) for the respective lectures.<\/p>\n

Finally, we should mention swirl (statistics with interactive R programming). swirl is an intelligent tutoring system developed by Nick Carchedi, with contributions by Sean Kross and Bill and Gina Croft. It offers a way to learn R in R. Download swirl here<\/a>. There\u2019s a swirl module for this course<\/a>!. Try it out, it\u2019s probably the most effective way to learn.<\/p>\n

Statistical inference defined\u00a0<\/b><\/p>\n

Watch this video before beginning.<\/a><\/p>\n

We\u2019ll define statistical inference as the process of generating conclusions about a population from a noisy sample. Without statistical inference we\u2019re simply living within our data. With statistical inference, we\u2019re trying to generate new knowledge.<\/p>\n

Knowledge and parsimony, (using simplest reasonable models to explain complex phenomena), go hand in hand. Probability models will serve as our parsimonious description of the world. The use of probability models as the connection between our data and a populations represents the most effective way to obtain inference.<\/p>\n

Motivating example: who\u2019s going to win the election?\u00a0<\/b><\/p>\n

In every major election, pollsters would like to know, ahead of the actual election, who\u2019s going to win. Here, the target of estimation (the estimand) is clear, the percentage of people in a particular group (city, state, county, country or other electoral grouping) who will vote for each candidate.<\/p>\n

We can not poll everyone. Even if we could, some polled may change their vote by the time the election occurs. How do we collect a reasonable subset of data and quantify the uncertainty in the process to produce a good guess at who will win?<\/p>\n

Motivating example, predicting the weather<\/b><\/p>\n

When a weatherman tells you the probability that it will rain tomorrow is 70%, they\u2019re trying to use historical data to predict tomorrow\u2019s weather – and to actually attach a probability to it. That probability refers to population.<\/p>\n

Motivating example, brain activation\u00a0<\/b><\/p>\n

An example that\u2019s very close to the research I do is trying to predict what areas of the brain activate when a person is put in the fMRI scanner. In that case, people are doing a task while in the scanner. For example, they might be tapping their finger. We\u2019d like to compare when they are tapping their finger to when they are not tapping their finger and try to figure out what areas of the brain are associated with the finger tapping.<\/p>\n

Summary notes\u00a0<\/b><\/p>\n

These examples illustrate many of the difficulties of trying to use data to create general conclusions about a population. Paramount among our concerns are:<\/p>\n