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.
The Carpentry Class: A Fable
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 he thought he’d build one.
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 piles 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. 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.
Building a house requires the knowledge of how walls are constructed, the ability to use the tools, and the practical skills to implement the techniques.
The Statistics Myth: Having knowledge about statistics is the only thing necessary to practice statistics.
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, because students need to acquire the knowledge before they 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.
But researchers (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 beginning researcher 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 look stupid. 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 researchers 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 they are leaning as they are doing, and anyone learning a practical skill needs timely, practical, accurate support to learn.