What's Ailing Introductory Statistics?

Introductory courses are the most important in any academic department. They are often a student's first and only exposure to a discipline, and reach the widest audience of any course in the department. A bad first impression can not only cheats people out of a potential career option, but can leave them with a lasting aversion to an entire field. Introductory statistics has the added importance of being the basis of every scientific field – no pressure there.

So why are there so many introductory statistics horror stories? Introductory statistics has a high conceptual overhead, but very little computational demands – you can get very far with basic arithmetic, and even further with a little calculus. Without a firm grasp of the conceptual basis, introductory statistics can easily become a vacant exercise in arithmetical procedures.
How do we keep introductory statistics from becoming a rote arithmetic exercise, devoid of all its utility? In my experience, there are a couple concepts that people don’t seem to be grasping and retaining:

• What is randomness? Where does it come from? How does it fit into research?
• What’s the difference between probability and statistics?
• What does a null hypothesis mean? Why can we only collect evidence against it?
• What are Type I Error and Power? What’s the intuition behind test statistics and null distributions?

There are a lot of challenges in constructing a good introductory statistics curriculum. Lots of introductory statistics books are garbage, and developing good lecture material takes a lot of time, which is often in very short supply. We have a wide audience to reach, from the next generation of statisticians to those who just care about degree requirements and letter grades – our job is to convert as much of the latter as possible into the former. This is a formidable task, but not an impossible one.

Fortunately, the tools available to teachers are evolving. I think R has the potential to make a huge impact in statistics education at all levels, for a number of reasons:

·       Free – students can use it outside of computer labs and after they graduate.

·       Open source – makes tinkering easier, and that’s the best way to learn a great deal in a limited time.

·       Community – Users groups, bloggers, free open courses, and more, all lending support.

·       R Markdown – weave R into blogs, labs, presentations, and such. When people are copying from a blackboard or slides, they’re not spending as much effort listening.

·       Shiny – This seems like an incredibly powerful tool for creating and disseminating interactive didactic tools which previously were only accomplished using JS.

R isn’t the only game in town, but I do think it’s the best way forward for students.

The S Word

When people ask what I do for a living, I initially avoid outright use of the "S word" - statistician. I'm quite proud of my profession, but just from personal experience, using the S word invokes three common responses:

• "I hated Intro Stat." (followed by a rant about their horrific ordeal)
• "There are three kinds of lies: lies, damned lies, and statistics." / "You can prove anything with statistics."
• Suppressing the reflexive groan hasn't become any easier with practice.
• "Oh that's nice," followed by either:
• "So what exactly does a Statistician do?"
• (More frequently) An abrupt change of topic

I don't want to sound cynical, but just from personal experience, statisticians appear to be in the same boat as dentists- usually the only people not apathetic to your profession are those trying to score a free consult. While people seem to know what dentists do, and some may have a modicum of respect or appreciation for the value of dentistry, the same can't generally be said for statisticians. Very few people seem to understand exactly what statisticians do, and how our profession fits into society. This is why I usually refer to myself as a "medical researcher" - this doesn't seem to carry as much emotional baggage as the S word, people generally understand the value of medical research, and this tends to sustain enough of a conversation to discuss why statistics and statisticians matter.