# 1: Introduction

Learning Objectives

Having read this chapter, you should be able to:

• Describe the central goals and fundamental concepts of statistics
• Describe the difference between experimental and observational research with regard to what can be inferred about causality
• Explain how randomization provides the ability to make inferences about causation.

“Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.” - H.G. Wells

• 1.1: What is statistical thinking?
Statistical thinking is a way of understanding a complex world by describing it in relatively simple terms that nonetheless capture essential aspects of its structure, and that also provide us some idea of how uncertain we are about our knowledge. The foundations of statistical thinking come primarily from mathematics and statistics, but also from computer science, psychology, and other fields of study.
• 1.2: Dealing with statistics anxiety
Anxiety feels uncomfortable, but psychology tells us that this kind of emotional arousal can actually help us perform better on many tasks, by focusing our attention So if you start to feel anxious about the material in this course, remind yourself that many others in the class are feeling similarly, and that the arousal could actually help you perform better (even if it doesn’t seem like it!).
• 1.3: What can statistics do for us?
There are three major things that we can do with statistics: (1) Describe: The world is complex and we often need to describe it in a simplified way that we can understand. (2) Decide: We often need to make decisions based on data, usually in the face of uncertainty. (3) Predict: We often wish to make predictions about new situations based on our knowledge of previous situations.
• 1.4: The big ideas of statistics
There are a number of very basic ideas that cut through nearly all aspects of statistical thinking. Several of these are outlined by Stigler (2016) in his outstanding book “The Seven Pillars of Statistical Wisdom”, which I have augmented here.
• 1.5: Causality and Statistics