A.2 Course objectives: what is this course for?

A.2.1 The big picture

This course is called “Introduction to the Ideas and Applications of Statistics,” which is kind of long, but then, that’s what it is.

The goal is that at the end of this course you will be prepared both to succeed in a follow-up stats course (typically Stat 242, though you may also be prepared for certain stats electives) and – and this is honestly more important – to interact with the stats and data you run across in your own life.

Maybe you plan to go on in statistics (or maybe you will by the end of the semester!). Maybe you want to improve your data skills for use in another science, or even the humanities. Maybe you don’t intend to do any of that at all – but you’re still going to be in a position to read the news, to vote, to hire people or decide between products, to interpret medical advice, and so on. Understanding the basics of statistics helps you do that in a more informed and well-grounded way, and helps you convince other folks of your ideas.

That last part, by the way, is why we’ll spend so much time building up your statistical communication skills – being able to talk about what you know and don’t know (and how you know), and relate what someone is telling you to what you’ve already learned.

Not to be too dramatic, but…statistics gives you a different way of seeing the world. And that’s a challenging thing to learn! But it’s also very powerful. It’s a whole new way of understanding what’s happened in the past, and what might happen next.

A.2.2 Specific objectives

The key statistical tools we’ll work with in this course are divided into 6 chunks, or modules, each of which focuses on a different set of topics and skills. You can see the list of modules below; you’ll get more details about what each one involves as we get closer to it.

You’ll build your application skills by working on an analysis of your own dataset in the Project, and your communication skills by interacting with your classmates (and me!) throughout the semester, both formally and informally.

The six modules are:

  • Welcome to Data
  • Descriptives
  • Probability
  • Inference Basics
  • Inference Variations
  • Regression for Realsies

There’ll be a bit of extra time at the end of the course, which we’ll use for a combination of additional topics (not covered by Assessments, though possibly on the Final Check), review, and project presentations.

Let me take this moment, though, to remind you that I’m neither omniscient nor omnipotent. Things might change :)