Information scientists tell us that data, alone, have no value or meaning [1]. When organized and interpreted, data become information, which is useful for answering factual questions: Which is bigger, X or Y? How many Z's are there? A body of information can be further transformed into knowledge, which reflects understanding of how and why, at a level sufficient to direct choices and make predictions: which algorithm should I use for this application? How long will it take to run?

Data analysis is a process of inspecting, summarizing, and interpreting a set of data to transform it into something useful: information is the immediate result, and knowledge the ultimate goal.

This chapter surveys some basic techniques of data analysis and illustrates their application to algorithmic questions. Section 7.1 presents techniques for analyzing univariate (one-dimensional) data samples. Section 7.2 surveys techniques for analyzing bivariate data samples, which are expressed as pairs of (X, Y) points. No statistical background is required of the reader.

One chapter is not enough to cover all the data analysis techniques that are useful to algorithmic experiments – something closer to a few bookshelves would be needed. Here we focus on describing a small collection of techniques that address the questions most commonly asked about algorithms, and on knowing which technique to apply in a given scenario.