Statistics is the science of data collection and data analysis. We provide, in this chapter, a brief introduction to principles and techniques for data collection, traditionally divided into survey sampling and experimental design --- each the subject of a rich literature. While most of this book is on mathematical theory, covering aspects of Probability Theory and Statistics, the collection of data is, by nature, much more practical, and often requires domain-specific knowledge. And careful data collection is of paramount importance. Indeed, data that were improperly collected can be completely useless and unsalvageable by any technique of analysis. And it is worth keeping in mind that the collection phase is typically much more expensive that the analysis phase that ensues (e.g., clinical trials, car crash tests, etc). Thus the collection of data should be carefully planned according to well-established protocols or with expert advice. We discuss the basics of data collection in this chapter.

This chapter introduces Kolmogorov’s probability axioms and related terminology and concepts such as outcomes and events, sigma-algebras, probability distributions and their properties.

In this chapter we introduce and briefly discuss some properties of estimators and tests that make it possible to compare multiple methods addressing the same statistical problem. We discuss the notions of sufficiency and consistency, and various notions of optimality (including minimax optimality), both for estimators and for tests.

In a wide range of real-life situations, not one but several, even many hypotheses are to be tested, and not accounting for multiple inference can lead to a grossly incorrect analysis. In this chapter we look closely at this important issue, describing some pitfalls and presenting remedies that `correct’ for this multiplicity. Combination tests assess whether there is evidence against any of the null hypotheses being tested. Other procedures aim instead at identifying the null hypotheses that are not congruent with the data while controlling some notion of error rate.

Randomization was presented in a previous chapter as an essential ingredient in the collection of data, both in survey sampling and in experimental design. We argue here that randomization is the essential foundation of statistical inference: It leads to conditional inference in an almost canonical way, and allows for causal inference, which are the two topics covered in the chapter.

Estimating a proportion is one of the most basic problems in statistics. Although basic, it arises in a number of important real-life situations. Examples include election polls, conducted to estimate the proportion of people that will vote for a particular candidate; quality control, where the proportion of defective items manufactured at a particular plant or assembly line needs to be monitored, and one may resort to statistical inference to avoid having to check every single item; and clinical trials, which are conducted in part to estimate the proportion of people that would benefit (or suffer serious side effects) from receiving a particular treatment. The fundamental model is that of Bernoulli trials. The binomial family of distributions plays a central role. Also discussed are sequential designs, which lead to negative binomial distributions.

We consider an experiment that yields, as data, a sample of independent and identically distributed (real-valued) random variables with a common distribution on the real line. The estimation of the underlying mean and median is discussed at length, and bootstrap confidence intervals are constructed. Tests comparing the underlying distribution to a given distribution (e.g., the standard normal distribution) or a family of distribution (e.g., the normal family of distributions) are introduced. Censoring, which is very common in some clinical trials, is briefly discuss.