Whether you took or are taking an introductory course on statistics, you probably didn’t get to choose from a dozen candidate offerings. You had to take the specific course required for your major. You can, though, evaluate what you got. Did you get your money’s worth from that introduction to statistics class? Here are a few things to think about.
Why did you take Statistics 101? Was it a requirement for a degree? Many majors, especially for advanced degrees, require some statistics (https://statswithcats.wordpress.com/2010/06/08/why-do-i-have-to-take-statistics/). Was it a less frightening alternative to other courses? Statistics can be used as a substitute for calculus in some undergraduate programs and for a foreign language in some Ph.D. programs. Or, maybe you didn’t have any expectations other than to learn something new.
I took statistics thirty-five years ago when I was majoring in geology, which doesn’t have a lot of quantitative deterministic theories for describing natural processes. Even today, predicting earthquakes, landslides, volcanic eruptions and other earth phenomena remain elusive goals. I wanted to learn how to develop mathematical equations, models, to explain and predict phenomenon. Regression analysis turned on the light bulb over my head. That’s how I got here.
When you buy an expensive product at a store, you usually have some expectations of what you should get for your money. When you buy a new car, for instance, you may want it to look and handle a certain way. You may not voice your expectations, or even be able to describe what you want, but you do have expectations. Your Stats 101 course is similar. You paid a lot of tuition to take the course; you must have had some expectation, even subconscious, of what you would take from it. This is important because it sets a reference point for what you experience in the course. So ask yourself, did your course give you what you expected, and just as important, were your expectations reasonable?
Think of the person who taught you in Statistics 101. How would you rate him or her on these four criteria?
- Knowledge—Knowledge may be the first thing you think about when you think of a college professor, and for that reason, it’s probably the least important discriminator of instructor quality. They all have adequate knowledge, at least from your level of understanding. It’s what the instructors do with their knowledge that makes the differences.
- Communication Skills—Being able to convey knowledge is a necessity for an instructor. Some instructors communicate information better than others, and unfortunately, some instructors do not communicate well at all. They may be inarticulate, have an accent or a speech defect, be speaking in a second language, or just not be able to explain difficult concepts or answer questions well.
- Engagement—Instructors usually teach the same courses over and over again. Some instructors add content and try new descriptions with each class. Others use the repetition to become automatons, teaching the same content in the same way year after year, even to the point of reading their past lectures.
- Empathy—Instructors have an obligation to teach certain content but they also should be sensitive to what their students want to learn and need to learn to further their careers. Empathetic instructors might try to tailor their teaching to the interests of their students, like citing examples from the disciplines of their majors. Oblivious instructors will teach about their latest interests, regardless of the applicability to the students.
The minimum requirements for an instructor are to know the subject and be able to communicate that knowledge. What separates the best instructors from the rest are their level of engagement and their empathy to the needs of the student. So ask yourself, did your instructor convey his excitement over what he was teaching? Did you leave the class curious about what else there might be to learn about statistics and about how you could use statistics yourself?
Artists draw, chefs cook, and statisticians calculate, but they do so in many ways. When I was learning statistics, my choices for doing calculations were a very unfriendly mainframe computer, a hand calculator, or pencil and paper. This choice may be why there are so few old statisticians around today.
How your instructor had you calculate statistics says something not only about the times but also about his level of empathy. Here’s why:
- Pencil and paper—No professional statistician calculates any serious statistics nowadays by hand, except perhaps on drink-stained cocktail napkins. Still, many instructors want their students to get the hands on feel of number manipulation. That’s valid. If it goes beyond probabilities, descriptive statistics, and simple tests of hypotheses, your instructor is a sadist.
- Calculator— No professional statistician calculates any serious statistics nowadays with a calculator, unless cocktail napkins are not available. If you plan on being an artist or a chef, manual calculations are fine. If you have any intentions of using statistics in your major, you need to learn software.
- Spreadsheet Software—Spreadsheet software is probably the best choice for most students. It can be used to set up and edit datasets, calculate statistics, and prepare graphs. Plus, it’s relative easy to use, and likely to be available to the student at school, work, and home.
- Statistical Software—Statistical software can be another good choice, depending on the learning curve. Simple statistical software can allow students to concentrate on interpreting statistics instead of calculating them. Advanced statistical packages like SAS and SPSS, while necessary for advanced courses, are beyond what introductory students need to learn unless they plan a career in statistics. Because of the cost of these packages, they are not likely to be available to the student at work and at home.
- Programming—If you plan on a career in statistics, you will probably learn the R language or some other coding tool. If you learn it in Stat 101 and you are not a statistics major, you have to wonder what your instructor is thinking, if he is.
So, was your instructor thinking about your needs when he decided how the class would do calculations? If you can’t use his method of choice in the future, it’s kind of a wasted effort.
Concepts vs. Skills vs. Thinking
In designing your Stats 101 course, the instructor had to decide how to proportion class time between teaching concepts, skills, and statistical, thinking.
Concepts are the whys of statistics. They are the reasons why statistics work. Examples include populations, probability, the law of large numbers, and the central limit theorem. Instructors tend to devise games and demonstrations to help students remember fundamental concepts. Learning statistical concepts is beneficial to statistics majors and non-majors, both in school and in later life.
Skills are the whats and hows of statistics. They involve calculations, like probabilities, descriptive statistics, and simple tests of hypotheses. Skills are learned by repetition. You learn them by doing the calculations in the homework assignments, at least the even-numbered problems. After Stats 101, skills like designing data matrices for a particular analysis are much more important than the calculations themselves, which are usually carried out by software.
Concepts and skills account for the majority of Stat 101 classes. This is perhaps unfortunate, for the greatest need in society is for people to understand statistical thinking. Statistical thinking involves understanding how to define a problem in light of some objective, what uncertainty and risk are and how they can be controlled, and the difference between significance and meaningfulness.
So, did the things you learned in Statistics 101 mostly involve concepts, skills, or statistical thinking? What things were you able to take from the class and use in later life?
What Do You Think?
Now all of this ignores course content. That’s a BIG topic for another time. For now, think about what your introduction to statistics course was like. Was what you expected? What would have made a better Stat 101 for you?
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I agree with most of what you’re saying, but I question the idea of not using r unless you are working only as a statistics major. I use it in my classes, even at the high school level, as a tool for finding things like the standard deviation or the mean or the five point summary. I don’t use it as a tool for programming the more complicated stuff, but i find that it makes better graphs than excel or other such models. I also like that you can work with multiple sets of data using a .csv file.
Is working with r really that much more complicated when making a graph than excel is? If it is, can you tell me in what ways I can help shape the course so as to make those areas easier? Certainly some things look harder (for example, making a histogram can seem frustrating at first, but the more you practice . . .) but when compared to making a graph approachable as a finished product, I find r more elegant and easier to manipulate.
But maybe I’m just stuck in my little r tower.
I have two concerns about using R for a class on introductory statistics, especially for non-majors.
First, statistics is intimidating enough, why add coding on top of it when there are GUIs available? Back in the 1970s, there were no alternatives. Even SAS and SPSS required you to write code. But today, there are GUIs for everything, even R and many programming languages.
Consider the analogy of automobile transmissions. Most people learn to drive on cars with automatic transmissions because they are easier. There’s so much other stuff a novice driver has to learn, learning how to shif gears can be deferred. Automatic transmissions are more expensive to buy and maintain, and don’t perform as well as manual transmissions. Still, most consumers (http://www.metafilter.com/52324/The-decline-of-manual-transmission-cars) buy cars with automatic transmissions, while professionals (truck drivers, NASCAR) use the more efficient manual transmissions.
Is R better than software like Excel? I think it depends on the person. You can make your data jump through R hoops. I like excel and those automatic-transmission statistical packages because I can do everything I need to do with them. They are set up as matrices, so it’s easier for me to visualize, design, and edit data. Furthermore, if I can’t present the results in Excel, my audience at work isn’t likely to understand it. At the same time, I find Excel’s capabilities to configure and annotate basic graphs to be unsurpassed by any statistical software. If I need a Chernoff face, OK, then I have to use Statistica. But the issue isn’t you or me, it’s what would be best for students.
That brings me to my second point. In the business world, workers collaborate on many projects through the use of electronic files. Every business owns some brand of spreadsheet software and every business professional has some level of expertise with spreadsheets. Wouldn’t it be better to polish a student’s ability with something they’ll almost certainly use in the future?
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