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 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.
The Statistics Myth:
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:
- a belief you can do it
- a commitment to best practices
- experience in applying the skills in different situations
- proficiency in using the tools
- a resource library
- ongoing training to learn new skills
- (ideally) a mentor to guide you as you practice.
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.
Thank you for your understanding and eye opening understanding
Excellent analogy. Analytics is very much about data storytelling and the story makes statistics relatable.
Your article was great. I learned some data analyzing using Excel and statistic. My question is which the best websites to practice data analyzing .
Do you have any materials on mathematical statistics? My university courses are taught poorly and aren’t catered toward students without a strong mathematical background. I also feel like it’s not so useful to learn how to solve the types of problems one would find in a Rice book, for instance, at the snap of your fingers, unless that is exactly what one wants to do, and that understanding the concepts/having an idea of the mechanics is good enough. It seems like this mindset is somewhat controversial, as there are both theoretical and applied programs. What do you think?
Harv,
We don’t. Our focus here is on helping people analyze data for their research, not classes.
But I feel your pain. Mathematical statistics courses are very tough. I will say, though, that as a practicing statistician, having that theory background makes a big difference in ways you wouldn’t expect.
I survived them by spending lots and lots of time in my TA’s office hours. That’s what I would suggest.
Quite useful details about statistics. I’d also like to add one point. If you need professional help with a statistics project? Find a professional in minutes!
I have completed my class 12th and want to pursue statistics because of its flexibility and usefulness in various fields as I don’t know which career should I pursue. I really liked your answer but I am confused, I will be doing a bsc in statistics but how and where I should start applying it. How should I start with applying.
I have another query, please someone help. Bsc in statistics or bsc in applied statistics. What should I do?
Ishika,
The best way to do it is to start analyzing data. A job as a data analyst would be ideal, but if that’s not an option, start volunteering. There are a number of volunteer organizations, like Statistics without Borders and Peace-Work who are always looking for volunteers.
This helped so much. I’m delving into the world of statistics and analysis and this was just what I needed to pick myself up and get back on track.
Karen
I’m two months away from majoring in Mathematical Statistics on BSc Actuarial Science course. I’m partly terrified of the unknown of the Actuarial mathematics and Statistics mainly because most of the people who matriculated with a distinction in math are/ were finding the subject (module) daunting. Your article today has put to rest my worries. I now know what to do. After all, I am a son of a “master carpenter”.
Khotso, that’s great! I found that course very difficult, but I made it through and use what I learned there all the time. Just don’t be afraid to ask lots and lots of questions.
Thank you
Thank you for such an eye opening view. It is true, because it is the actual ground where you have to fight the battle after having training. So until you do not apply, how you can know you have learnt or not. Also the things vary from research to research.
I fully concur with this view and appreciate the analogy. In class we use dummy data which usually deviates from the reality experienced with live data.
Dear Karen,
This article really nice and a confidence booster.
Thanks,
Subhendu
When is this published?
Good article! It’s true that what we learn in class doesn’t always apply exactly to practical situations.we learn more while practicing.
I agree with you Karen that most people don’t really learn statistics until they start analyzing data in their own research. When you start your own work and analyzing data, then you better understand that how to apply and where to apply statistics formula.
Wow you are so right i loved your post.
I emailed certain phrases to my Statistics Professor
Try speaking the English language without knowing the words. Some things need to be known and understood before concepts can be applied.
Absolutely agree, Mike. The frustration in learning how to apply happens when people think the knowledge is all one needs. It’s necessary, but not sufficient.
Good article! It’s definitely true that what you learn in class doesn’t always apply exactly to practical situations.
you are a genius karen, +1 to you
Well done! Your example demonstrated your zeal to bring real world considerations into the abstract world of statistics. This is my first visit and I’m excited to see your other contributions to the world of statistics. Thank you
You “nailed” it. The above can apply equally as well to any other worth while endeavor but I know of no other that it applies more than statistics.