Someone recently asked me if they need to learn R. In responding, it struck me that this is another way that learning a stat package is like learning a new language.
The metaphor is extremely helpful for deciding when and how to learn a new stat package, and to keep you going when the going gets rough. (more…)
In Part 6, let’s look at basic plotting in R. Try entering the following three commands together (the semi-colon allows you to place several commands on the same line).
x <- seq(-4, 4, 0.2) ; y <- 2*x^2 + 4*x - 7
plot(x, y) (more…)
I once had a client from engineering. This is pretty rare, as I usually work with social scientists and biologists. And despite the fact that I was an engineering major for my first two semesters in college, I generally don’t understand a thing engineers talk about.
But I digress. In this consultation, we had gotten about 20 minutes into me not understanding a word he was talking about when I realized he was using “Parameters” when he meant “Variables.” As in, “I measured four flexibility parameters on the doohickey.”
In statistics, Variables are things you measure that vary from observation to observation. Height, weight, flexibility, bending strength, % ground cover–these are all Variables if they vary from one observation to another. (They are constants if they don’t).
Parameters are things you measure about the variables. Their means, their variances, the size of their effect on another variable. And parameters specifically refer to the measurements made about the entire population.
I suppose it makes sense that engineers consider variables to be parameters, since to them, parameters are things you measure about doohickeys. In statistics, variables are the doohickeys getting measured.
So it makes it hard to talk with engineers because I have to translate as they speak. But I’ve come to accept that they speak a different language although with the same words.
But lately, I’ve seen other people (like ecologists) calling their variables Parameters. And in the same sentence as using the terms like p-value and adjusted R-squared, so I know they knew statistics well.
What’s going on?
There are probably many myths about statistics, but there is one that I believe leads to the most frustration in researchers (and students) as they attempt to learn and apply statistics.
There was once a man who needed to build a house. He had a big pile of lumber, and he needed a place to live, so building one himself seemed like a good idea.
He realized that he did not have the knowledge and many skills needed to build a house.
So he did what any intelligent, well-educated person would do. He took a course: House Building 101.
There was a lot of new jargon: trusses, plumb walls, 16” on center, cripple studs. It was hard to keep it all straight. It didn’t make sense. Why would anyone ever need a header anyway?
But he made it through with a B+. He learned the basics. The doghouse he built in the lab was pretty straight. He even took another course to make sure he knew enough: Advanced Carpentry.
It was time for the man to build his house. He had his land, his plan, his tools, his sacks of concrete, windows, lumber, and nails.
The first day he started with enthusiasm. He swung his hammer with gusto and nailed his first wall into place. It felt good.
But wait. His house was being built on a hill. The textbook only had flat land. How should he deal with hills?
And this house has a bay window. His doghouse had only double hung windows. Doesn’t a bay window stick out?
And he was not sure which technique to use to make that 145 degree angle in the hall. The courses never mentioned anything but 90 degree angles.
In class, they used circular saws. In order to install the trim he ordered, he needed to use a chop saw and a table saw.
He didn’t realize he was supposed to put in the plumbing before the electric, so he ended up doing a LOT of rewiring when the plumbing wouldn’t fit around his wires.
Even with the plans in front of him, there were so many decisions to make, so many new skills to learn.
And he was supposed to move into the house in 4 months when his lease ran out. He’d never get it done in time. Not on his own.
He sounds like a fool, doesn’t he? No one could build a house after taking even a few courses. Especially not with a deadline.
Building a house requires the knowledge of how walls are constructed, sure. But it also requires the ability to use the tools, and the practical skills to implement the techniques.
We can see that this project was a silly one to tackle, yet all the time we think it’s our fault that we have trouble with statistical analysis after taking a few classes.
Having knowledge about statistics is the only thing necessary to practice statistics.
This isn’t true.
And it’s not helpful.
Yes, the knowledge is necessary, but it is not sufficient.
Statistics doesn’t make sense to students because it is taught out of context. Most people don’t really learn statistics until they start analyzing data in their own research. Yes, it makes those classes tough. You need to acquire the knowledge before you can truly understand it.
The only way to learn how to build a house is to build one. The only way to learn how to analyze data is to analyze some.
Here’s the thing. Data analysts (and house builders) need practical support as they learn. Yes, both could slug it out on their own, but it takes longer, is more frustrating, and leads to many more mistakes.
None of this is necessary. There can be a happy ending.
Carpenters work alongside a master to learn their craft. I have never heard of a statistician or a thesis advisor who sits next to a novice analyzing data. (Anyone who had an advisor like that should consider themselves lucky). Unlike a novice carpenter, a novice data analyst is not helpful. They can’t even hold the ladder.
More common are advisors who tell their students which statistics classes to take (again, if they’re lucky) then send them off to analyze data. The student can ask questions as they go along if they are not too afraid to admit what they don’t know. And if their advisor is available. And knows the answer.
Really good advisors are not too busy to answer in a timely manner and are willing to admit it if they don’t know the answer.
But most data analysts feel a bit lost. Not just new ones—many experienced researchers never really learned statistical practice very well in the first place. Nearly all researchers face new statistical challenges as their research progresses, and it’s often difficult to find someone knowledgeable enough who is willing to and able explain it.
They are not lost because they are stupid.
They are not lost because statistics is beyond their capabilities.
They are not lost because they didn’t do well in their statistics classes.
They are lost because like carpentry, statistical analysis is an applied skill, a craft.
Acquiring the background knowledge is only one essential part of mastering a craft.
Others include:
Think about it. How many skills (dancing, sailing, teaching) have you acquired in your life by only taking a class that gave you background knowledge, but no real experience and no real mentor to coach you?
So if you’re stuck on something in statistics, give yourself a break. You can do this with the right support.
Everything we do at The Analysis Factor is to help you get unstuck. If you’re frustrated, tired, or even scared…there is another way.
If you need help right now, we’ve got your back. Please check out our Statistical Consulting services and our Statistically Speaking membership.