Statistical Techniques

The discipline of statistics has long addressed the same fundamental challenge as data science: how to draw robust conclusions about the world using incomplete information. One of the most important contributions of statistics is a consistent and precise vocabulary for describing the relationship between observations and conclusions. This text continues in the same tradition, focusing on a set of core inferential problems from statistics: testing hypotheses, estimating confidence, and predicting unknown quantities.

Data science extends the field of statistics by taking full advantage of computing, data visualization, machine learning, optimization, and access to information. The combination of fast computers and the Internet gives anyone the ability to access and analyze vast datasets: millions of news articles, full encyclopedias, databases for any domain, and massive repositories of music, photos, and video.

Applications to real data sets motivate the statistical techniques that we describe throughout the text. Real data often do not follow regular patterns or match standard equations. The interesting variation in real data can be lost by focusing too much attention on simplistic summaries such as average values. Computers enable a family of methods based on resampling that apply to a wide range of different inference problems, take into account all available information, and require few assumptions or conditions. Although these techniques have often been reserved for graduate courses in statistics, their flexibility and simplicity are a natural fit for data science applications.