HBR: If I’m an average professional or an executive, I’ve read your book, I know this stuff matters and I also know it’s complicated and I can only expect so much. Is there such a thing as kind of a level of statistical literacy that I need to get to? What kind of education do I have to go back and make sure that I have?
Silver: I think the best training is almost always going to be hands on training. In some ways the book is fairly abstract, partly because you’re trying to look at a lot of different fields. You’re trying not to make crazy generalizations across too many spheres.
But my experience is all working with baseball data, or learning game theory because you want to be better at poker, right? Or [you] want to build better election models because you’re curious and you think the current products out there aren’t as strong as they could be. So, getting your hands dirty with the data set is, I think, far and away better than spending too much time doing reading and so forth.
HBR: What about if I’ve read your book and I’m just starting college or a little younger and I’m trying to think actually maybe this statistician/data scientist role is something that I’m interested in? What do I study? How much education do I need? What’s that base for plugging into some of these jobs?
Silver: Again, I think the applied experience is a lot more important than the academic experience. It probably can’t hurt to take a stats class in college.
But it really is something that requires a lot of different parts of your brain. I mean the thing that’s toughest to teach is the intuition for what are big questions to ask. That intellectual curiosity. That bullshit detector for lack of a better term, where you see a data set and you have at least a first approach on how much signal there is there. That can help to make you a lot more efficient.
That stuff is kind of hard to teach through book learning. So it’s by experience. I would be an advocate if you’re going to have an education, then have it be a pretty diverse education so you’re flexing lots of different muscles.
You can learn the technical skills later on, and you’ll be more motivated to learn more of the technical skills when you have some problem you’re trying to solve or some financial incentive to do so. So, I think not specializing too early is important.
- Rest of interview here with Nate Sliver, author of The Signal and the Noise: Why So Many Predictions Fail-but Some Don't
Silver: I think the best training is almost always going to be hands on training. In some ways the book is fairly abstract, partly because you’re trying to look at a lot of different fields. You’re trying not to make crazy generalizations across too many spheres.
But my experience is all working with baseball data, or learning game theory because you want to be better at poker, right? Or [you] want to build better election models because you’re curious and you think the current products out there aren’t as strong as they could be. So, getting your hands dirty with the data set is, I think, far and away better than spending too much time doing reading and so forth.
HBR: What about if I’ve read your book and I’m just starting college or a little younger and I’m trying to think actually maybe this statistician/data scientist role is something that I’m interested in? What do I study? How much education do I need? What’s that base for plugging into some of these jobs?
Silver: Again, I think the applied experience is a lot more important than the academic experience. It probably can’t hurt to take a stats class in college.
But it really is something that requires a lot of different parts of your brain. I mean the thing that’s toughest to teach is the intuition for what are big questions to ask. That intellectual curiosity. That bullshit detector for lack of a better term, where you see a data set and you have at least a first approach on how much signal there is there. That can help to make you a lot more efficient.
That stuff is kind of hard to teach through book learning. So it’s by experience. I would be an advocate if you’re going to have an education, then have it be a pretty diverse education so you’re flexing lots of different muscles.
You can learn the technical skills later on, and you’ll be more motivated to learn more of the technical skills when you have some problem you’re trying to solve or some financial incentive to do so. So, I think not specializing too early is important.
- Rest of interview here with Nate Sliver, author of The Signal and the Noise: Why So Many Predictions Fail-but Some Don't
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