Why Statistics & Data Science Matter - An Interview With Statistician Liberty Vittert
Liberty Vittert is the Mitchell Lecturer (Assistant Professor) at the University of Glasgow in the School of Mathematics and Statistics and a visiting Scholar at Columbia University in New York City.
She has significant experience in explaining issues concerning mathematics, statistics, polling and voting techniques, and the topic of “big data” to the media, public, and government. We interviewed her to find out why knowing your stats can make a huge difference to both your personal and business decisions!
Hi Liberty. What's your career highlight so far?
My father is my greatest audience. He thinks science is dumb, university is a waste of time, the news is (almost) all fake, and that climate change is most definitely not real (ever heard of the Ice Age?). If I can help him understand why statistics and data science is important and engage him with what data can tell us about public opinion, government policies, global business, and really, the future of our world, then I can engage anybody.
I give him all of my speeches, word for word, and every time there are mountains of interruptions, pauses, confusions, etc. to sort out until it’s perfect. Our longest discussion about a presentation was 7 hours. A month ago, it was down to 2 hours. My career highlight will be when he says “Liberty, I have nothing to add” (but I’m not holding my breath).
Why are maths and statistics so fundamental to our personal lives and society as a whole?
Data is a company’s greatest strategic threat, but also its greatest strategic opportunity. We live in a new world where numbers are everywhere; we collect more information than we ever thought possible.
Numbers are in everything we do. Should I take that umbrella? Nah. Here, you are calculating the risk and reward of getting wet versus having to carry the stupid thing around all day (and if you’re anything like me, most certainly forgetting it somewhere).
How did you get to work today? Were there multiple routes or methods of transport? You logically decided where to go in order to maximize your chance of getting to work on time and minimizing being stuck in traffic for a zillion hours. You did statistics today. Statistics takes past occurrences calculates the likelihood of an event’s future occurrence - basically the closest thing to a crystal ball that mankind will ever have. While I do see a psychic, I prefer using statistics as a crystal ball.
Can you give examples of when a failure to understand figures has led to problems?
There are so many examples of when a failure to understand figures has led to utter disasters for media, government, businesses that I can’t even begin to name them. More importantly, data has also been one of the greatest strategic opportunities for others.
I have one personal favorite: we were told last summer that chocolate reduces our risk of Alzheimer's…wooo hooo! Not so fast. I would have to eat 1 kg of milk chocolate a day (5600 calories) to even potentially get some kind of benefit. Now, a day or two of that, sounds pretty good, but I’m pretty sure I would die of something else way before I got Alzheimer’s if I ate that much chocolate.
Why do you think figures/data are so often feared/misinterpreted?
Ever heard the phrase, we fear what we don’t understand? Well, we don’t understand numbers and so, we are afraid of them. But understanding numbers isn’t doing sums, it is critical thinking, I promise. We got so overwhelmed with the numbers themselves, that we don't take a step back and throw in a little common sense. Common sense is the key to understanding numbers.
When explaining big issues related to maths/statistics/big data (and so on), how do you aim to make this both useful and easy for audiences to understand?
Numbers are more than useful, they are essential and if you want to deliver real value to your company and frankly, in your personal life, you must understand them. You don’t need to become a statistician, but you need to know what questions you should be asking of the data that is being used to make decisions. Questions, those are the key. When I am presented with some research, a news article, a study, a report, a new policy, I have a set of questions that I ask every time. They are embarrassingly simple, but they always are able to suss out the good from the bad. Using real-life examples, particular to one’s own industry, I show people what questions they should be asking - the questions that I ask.
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