Teaching out loud and becoming StatQuest | Josh Starmer | Data Science Hangout
ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Josh Starmer, Founder and Creator of the massively popular YouTube channel StatQuest with Josh Starmer, to chat about his process for simplifying complex technical topics, overcoming imposter syndrome and feeling cringe, the enduring value of foundational statistics over fleeting AI trends, and how to use AI tools responsibly without sacrificing true understanding. In this Hangout, we explore Josh's unique approach to teaching and learning difficult concepts. He reveals that he specifically avoids watching other videos on a subject to ensure his explanations remain original. Instead, he focuses on finding the "simplest possible example" that still highlights the method's utility—such as creating a neural network with only three data points to visualize the math. Josh emphasizes that the hardest part of his work is constantly asking, "Can I make it simpler?" and extracting the essence of an equation so that learners can "feel" the statistics rather than just memorizing them. Resources mentioned in the video and zoom chat: StatQuest with Josh Starmer (YouTube) → https://www.youtube.com/c/joshstarmer The StatQuest Illustrated Guide to Neural Networks and AI → https://www.amazon.com/dp/B0DRS71QVQ The StatQuest Illustrated Guide to Machine Learning → https://www.amazon.com/dp/B0BLM4TLPY The StatQuest Store → https://statquest.org/statquest-store/ Introduction to Statistical Learning (ISLR) → https://www.statlearning.com/ Grokking Machine Learning by Luis Serrano → https://www.manning.com/books/grokking-machine-learning Hands-On Large Language Models by Jay Alammar & Maarten Grootendorst → https://www.maartengrootendorst.com/book/ Statistical Methods for Research Workers by R. A. Fisher → https://link.springer.com/chapter/10.1007/978-1-4612-4380-9_6 If you didn’t join live, one great discussion you missed from the zoom chat was the "Smug Bayesian" vs. Frequentist debate, which sparked jokes about p-hacking and a community demand for "Smug Bayesian" t-shirts. Will Mike K. Smith answer this call?? Would you wear a Smug Bayesian shirt? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 01:47 "Who are you and how did you get to the YouTube space?" 05:38 "What was it that you really wanted to do as a career?" 07:52 "Have you got any top tips on how to make video editing easier?" 10:30 "What is your process for breaking down a difficult concept?" 15:56 "What's the most common statistical or machine learning mistake you see in real projects?" 19:40 "How do you keep up with the hands-on stuff?" 21:43 "What is the one book that every data science professional should read?" 26:13 "What statistical concept do people think they understand but almost always misuse?" 30:42 "What would be your advice for a new grad to break out as a data science professional?" 35:55 "What are your tips for generating confidence to teach out loud?" 40:18 "Do you ever feel cringe? How do you deal with that?" 42:25 "Do you have any career advice?" 45:28 "How have your musical talents helped with your work in data?" 47:25 "How would you integrate AI as a responsible assistant and where would you forbid it?" 47:43 "What data tools and IDEs do you use?" 52:25 "What is a memorable concept that you remember having a personal moment through teaching?" 53:10 "What are you most excited for in 2026?"
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Transcript#
This transcript was generated automatically and may contain errors.
Hey there, welcome to the Paws at Data Science Hangout. I'm Libby Herron, and this is a recording of our weekly community call that happens every Thursday at 12 p.m. U.S. Eastern Time. If you are not joining us live, you miss out on the amazing chat that's going on. So find the link in the description where you can add our call to your calendar and come hang out with the most supportive, friendly, and funny data community you'll ever experience.
Can't wait to see you there. I am so excited to welcome our guest today. It's Josh Starmer of StatQuest. Hi, Josh. I would love it if you could introduce yourself to everybody if they don't already know you. I'm so happy to be right here today talking with the data science meetup with my favorite company, People at Paws. It makes my software that I use every day. Hooray.
Josh's background and origin of StatQuest
So, hello. I'm Josh Starmer. My background is in music and computing. I kind of always have loved them both. The computing has often paid better and had better hours. So, I've focused a lot of my professional life on that side, and I have a strong musical hobby. I, golly, I took a statistics class in graduate school, and I fell in love with statistics, even though the class was absolutely horrible. It's like one of the worst classes I've ever taken. But it opened my eyes to things, to think about things in ways I'd never thought about them before, even though these are obvious things.
Like, it's kind of like, I almost feel embarrassed to say that before I took statistics, I'd never really thought about variation. I never really thought about like, oh, we can quantify. I mean, I knew that like, snowflakes were all different, but I didn't know we could quantify variation, and I didn't know we had tools out there that could help us make sense of that variation and help us make good decisions with it. And that's what I fell in love with, was this realization that like, yes, everything's different all the time, and I can use that information to my advantage. And I was like, wow, that's really cool. Because up until then, whenever I'd done math, math had always resulted in a very, a point estimate was what we call in statistics land, like a single number would be the result. And I was always like, well, that's the way math is. And then statistics was like, no, it's not. This is reality. This is the world. This is not heaven. And I was like, okay. And I just love that.
But I'll be honest with you. So statistics, even though it was a terrible class, I've always struggled with it as a subject. It's even struggling with that very first concept of like, oh, there's this thing called variation that we can quantify. That was hard for me. But everything's been hard for me. And so I kind of started creating, for many different reasons, I started creating a YouTube channel as a way to teach myself statistics in a way that made sense to me. I'm very visual. And I'm not someone who can just look at an equation and go, oh, that makes sense. I have to break it down into sort of a visual interpretation. And then I can go back to the equation and go, oh, this part of the equation corresponds to this part of this illustration or something like that. And I can understand it that way. And my video is my way of sort of teaching myself, but also not to skip too much of the background in this short introduction. I worked in a genetics lab doing data analysis for a long time, for 13 years. And I created the channel also to teach them and my coworkers sort of that what I was doing wasn't magic.
I absolutely like the things that I want to call out from that to highlight are that you struggled with anything at all, right? Because I think that people look at your YouTube channel, they look at people who they think just know a lot of stuff and they think this person must just get it easily. It's so wonderful to hear you say, I am good at explaining this because it's hard, because I had to work through understanding it myself. And now I'm just sending that forward, right?
It's so wonderful to hear you say, I am good at explaining this because it's hard, because I had to work through understanding it myself. And now I'm just sending that forward, right?
Well, I either wanted to be a rock star or you are now achieved. And I actually, I succeeded at that and realized it was not the job I wanted. But the other job I wanted was, I wanted to be a professor at a university. And I specifically wanted to have a laboratory that focused on sort of mathematical biology, did computer simulations of biology and organisms and organs as a way to prevent having to use animals as test subjects. I was like, what if we can have computer programs as test subjects? Can we model the biology accurately enough that we can use that instead of animals for understanding toxicity or whatever? And I had that dream. It's like a weird dream. But I think part of the dream came from me when I was like six or seven years old, misinterpreting what my dad did for a living, because I wanted to do what my dad did.
But eventually, for better and for worse, there's something I had to say goodbye to because I'm a terrible grant writer. Much better at making YouTube videos, it turns out. The horrible reality of academia is that you're actually sometimes just a grant writer.
Video production tips
I'd love to do more in terms of video training and stuff like that on various topics. But the editing and the kind of post-production side of things takes so much time. So have you got any top tips on how to kind of make it so that it's a little easier or shorten that time from idea to final video? I really wish I could. That would make my life so much better, too. There's no shortcut.
The way I make my videos is so primitive. I just make a PowerPoint presentation. I use a program called Keynote, but it's essentially a free knockoff of PowerPoint. And I just export the slides, and I import them into iMovie, and I just record my overdubs. It's a long process, and it takes forever. But I've been doing it for so long, so many years, that when I sit down, it's what I expect to do. So it never dawned on me that I could do something else or make it faster. So yeah, unfortunately, I can't give you a good answer for that.
Simplifying complex concepts
Big fan, Josh. I am just so impressed with your ability to explain difficult concepts. I've taught a machine learning course and had students watch some of your videos. And then I think to myself, this could be explained to a high schooler. So I'm wondering if you could go through your process of how you take these really high difficulty concepts and turn them into something that's accessible for so many people. Yeah, sure. I'd love to. So like I said, I'm a pretty slow learner. And so the first thing I have to do is sort of read everything I can about the subject.
And I'll be honest, I specifically avoid trying to watch videos about subjects. I'm afraid if I watch another person's video, I'll copy them. And that's the last thing I want to do. Because if somebody else is already doing a great job the way they're teaching, then that's great. I want everyone to go to that video. I don't want them to come to mine, which is just an echo of that one. And so I try to avoid personally, and it's just for personal reasons, because people always ask me like, what's your favorite YouTube channel for learning new concepts? And I'm like, I don't use any of them.
So I read everything I can. And then I try to, whatever it is, I try to come up with the simplest possible example that still highlights one key reason that whatever the method is, is useful. So for example, for when I made a video on neural networks, neural networks can fit straight lines to things. But that's not very interesting, because we've got linear regression, we got a million other tools that can fit straight lines to things. So what I wanted was to come up with the simplest example that could illustrate something that was a little more exciting. And so I came up with a data set that had three points. So one, a low point, a high point, and then going back down to a low point. So we had a nonlinear sort of effect. And it just has three data points. And then I was like, what's the simplest neural network that I could come up with, that will then fit a nonlinear shape to that, and accurately make predictions based on it.
And this was actually very challenging, because training a ridiculously simple neural network was non trivial, because I kept getting stuck in, when I was trying to find the optimal parameters for it, I kept getting stuck in really bad local minimum. And I just had to write a program that would just change the seed, the rent for random numbers, it's because neural networks start off with random numbers associated for the parameters, and then it optimizes from those random numbers. And so I had to, I read a program that just iterated through different seed numbers, like for 1000s, until I finally found a combination of parameters that would work. And what was cool about this, what the goal was to come up with something where everything could be visualized, and I could do all the math in front of people. And there was no sort of mystery. And there was no just sort of like this matrix times this matrix equals this matrix, and just sort of, which is cool, and an awesome and compact notation, but it's harder to glean sort of exactly what's going on, if you can't follow the math one step at a time.
And so I succeeded. And what was nice is this, this neural network had one input, which was corresponded to the x axis on a graph. And it had one output, which corresponded to a y axis on a graph. And so for every x axis value, I could look at the corresponding y axis, and we could actually see exactly what was happening, we could see how two different shapes, two different curves were getting added together to end up with this nice bell shaped curve that fit the training data. And it took a long, long, long time to come up with that. And I'm probably giving you infinitely more details than you actually need right now. But the idea in general is that I do struggle to come up with as simple an example as I possibly can think of. And that's actually probably the hardest part of what I do is coming up with an examples like, and I'm always challenging myself, can I make it simpler? Is there something I can remove from this? Can I is there is there more of an essence that I can extract from that equation? That really just gets at just the important things. And I'm always asking myself those questions.
Common statistical mistakes and the assumptions of linear regression
What's the most common mistake? Oh, golly. That's a, that's a hard question for me to answer. I think. And the subject, the subject I'm going to change it to is actually something I'm really proud of. So I'm writing a book on statistics and I'll be honest. I've been haunted by the assumptions of linear regression for probably the last 20 years. And the reason why I've been haunted by them so much is that I memorized them, but I never really understood them. I never, I, I, I could, I could repeat what they are and I can repeat basically what they mean, but it's not something that's internal to my being. And, and when I wrote my book, I actually left them out. And the reason why I left them out is back in the, don't tell anyone this, but back in my 13 years as a professional bio statistician, I often just ignored them because we just didn't have enough data to even care, you know? So in practical settings, I know a lot of people freak out about all those, those assumptions. And for the most part, nine times out of 10, your data is going to be fine. Um, and don't worry about it.
I came up with a way to at least for myself that I can now look at data and just go, Oh, I know that this is not a good, a good data set for regression. And I can tell you exactly why. And I can point it out. And it just means these predictions in this region are going to be terrible. And I came up with a way to like, feel it. And I no longer have to memorize these things. And instead I can just look at the data and go, yeah, this is why don't worry about it. I like, I overcame this thing that had been sort of like an itch I couldn't scratch for years and years and years. And I'm like, I finally, finally got it.
Keeping up with hands-on practice
Yeah, always. I'm always worried. And I've almost, so I used to work in a lab and I still go back to the lab on a fairly regular basis to present rough drafts of videos that I'm working on because they were my original audience back in the day. And there's still, I think they're the hardest people to explain because they basically hate the subject. And so they're just tough in so many ways. So if I can get something that they like, it's great.
So that's, I'm actually, so my old boss, the boss that let me go because I was terrible at writing grants, we still actually get together. He's one of the best influences I've ever had in my life. He's the one that said, look, you are bad at writing grants, but you're good at this YouTube thing. You need to give it a try. And so he pushed me out of the lab and I've, instead of sinking, I was able to swim. And I owe a lot of that to him. Anyways, me and him are going to start doing some longitudinal data analysis in the spring. And I'm so excited about it because I've also wanted to do a series on time series, longitudinal analysis techniques for forever. And so I'm just going to dive in it with him. He's got some data that he's been working on. So I'm going to get back into the game a little bit and we'll see how it goes.
Book recommendations
So a shameless self-promotion. I've written some books and I've actually, I've written two and I've got a book on statistics coming out and I absolutely adore my statistics book. I love it. But I also, I've got a book on neural networks and AI and I have a book on machine learning and all of my books are just, they're almost like, like, like graphic novels. It's all pictures. Cause that's, I think in pictures. So I just draw pictures and then I annotate the pictures. They're going to show you what's going on.
I've been for the past four years, I've been in the AI ML world sphere and I'm trying everything I can to get out of that sphere and get back into like hardcore statistics. Cause I, to be honest, I find that actually more interesting because the statistical methodologies are, I just find them more interesting at a fundamental level. Whereas AI, I feel like a lot of AI is just optimization. Like, can we do this with less RAM? Can we do this faster? And those tricks I think are cool, but they're very ephemeral, right? Cause five years from now, someone is going to even have a faster method and a better way of using RAM. But statistics has a way of being useful for a very long time. Like, like the, the, the discoveries that we make in statistics and the methodologies that we have, have staying power. And, and for that, I have a ton of respect for it. And that's why I'm trying to get back to it.
But statistics has a way of being useful for a very long time. Like, like the, the, the discoveries that we make in statistics and the methodologies that we have, have staying power.
Misunderstood statistical concepts: P values and confidence intervals
Well, there's, there's always the P value, right? I mean, that's notoriously bad. And actually, interestingly enough, like confidence intervals are so bad that I just left them out of my book. I don't want them to be part of the conversation because even if you understand them, nobody else will. So just do everybody a favor and don't ever publish a confidence interval. That may be a controversial opinion, but I just think it's for the best. I just know that people will misinterpret that thing. And, and just don't do it. Just let's just like start a new campaign. Just like no more confidence intervals.
And, and so P values along those lines, people, a lot of people, even if people understand the P value and they know exactly what it means, they still treat it as if it's all you need to know. And you don't also need to know the effect size, right? Because if the effect size is like minuscule, then it's who cares what the P value is, right? The P value is, is, is only useful in the context of knowing like what the R squared is or what the difference in means are. You have to give it context for it to have any kind of reasonable interpretation. And a lot of people just present it on its own. And, and you're like, no, don't do it.
And I think, and that's legitimate. I am, I think that's fine to, to be a smug Bayesian, but the, but let's be honest in the real world. And let's be, let's talk about the real world, not this little tiny Bayesian bubble we live in that where everything is obvious and easy to interpret in the real world. People are just doing those P values left and right. And that is the world we actually have to deal with in a practical way. And it's one of the reasons why I've, I'm planning on doing a series on Bayesian statistics because it is awesome and I'm a big fan of it. But it's one of the reasons why I've, I've, I've waited so long to do it is can you do Bayesian statistics in Excel easily? No. Okay. End of discussion.
Advice for new data science graduates
Yeah, for sure. Thank you so much, Libby. And I appreciate Joss very much because I think your content actually got me through grad school and I just graduated yesterday.
So my question is like, what would be your advice for a new grad, especially someone without industry experience, to break out as a data science professional, especially since the job market is brutal and competitive right now? Yeah. You know, again, I wish I had lots of personal experience that I could reflect and go, oh, I know this works or this doesn't work. I can just tell you what I've heard from other people, which is practice your skills out loud on GitHub. You know, build a portfolio of data analysis projects. You can get data sets from wherever and just show that you know what you're talking about.
The one thing they said was everyone thinks they have to know the latest AI thing these days. And they say that the reality is that it's not like that. You know, the reality is like, it's just like we were talking about with Bayesian versus the rural world. Most organizations are doing their statistics and their predictions in Excel, right? They've got a spreadsheet with two columns of data, you know, and so it's, you know, they were actually emphasizing that a lot of companies are actually looking for people that are just really solid on more conventional data analysis techniques. And maybe they've, you know, they can use AI tools to work faster, but they're not like, you know, putting prompt engineer on their resume or something like that.
Overcoming imposter syndrome and teaching out loud
We talk a lot about learning out loud and I'm working on that whole process, but arguably you took it further with the whole act of teaching out loud. How did you, you took it further with teaching out loud. What are your tips for like generating confidence with that act? But it's like, I do know these things, even though technically formally my background is X per se. How do you avoid imposter syndrome is basically.
So one thing that a lot of people assume about me that is not correct is I've actually never taught a course. So I've actually, the only teaching I've ever done has been a sort of informal, you know, you know, it during a lab meeting when I was at UNC or, and now making YouTube videos. And occasionally I'll give like a seminar and, but I've never actually taught a class. And so I've, I've had a lot of imposter syndrome myself. And, and how did I get it over, over it very slowly. And I still have it to a certain degree. I actually just had to present basic statistics to the statistics national honor society. And that was one of the most stressful things I've ever done in my whole life, because I was not a great statistician.
And so, so one thing I do, you know, with, you know, like, instead of just like, cause a lot of statistics is based on like theory that was developed or at least originally published a hundred, a hundred years ago. When I think about statistics, you know, you guys look at the theory and I can say, well, without a computer, you'd have to do it this way. And, you know, and it's very complicated. You have to do a lot of calculus and it's kind of a nightmare. And, but then I was like, but computers are so fast nowadays. We can do it. We can actually test the null hypothesis and the frequentist assumptions by just doing tons and tons and tons of these tests and just see what happens. And it's a trick that Bayesian people have been doing for years. And it's like, why aren't frequencies doing the same thing? Like frequencies are kind of smug. They're like, well, we've already done the math 50 years ago. So why would we do it again? But the, the thing is, is, is when you do it, when you do, when you test it out yourself, I can feel it. And when I can feel it, I can be confident in it. I'm like, look, I, uh, not only does the theory work out the way it's supposed to, but when I did that, right, wrote this program to like test the null hypothesis, the way, you know, and do it a billion times and blah, blah, blah. I got the exact same number. And when I do it that way, I can feel it. And when I can feel it, I feel real comfortable talking about it in front of other people, unless it's the national statistics honor society.
Dealing with cringe and the value of imperfection
I like, uh, knowing that you get shaky too, because I get shaky every day before the hangout. And there was a question, an anonymous one as well. That was like, uh, do you ever feel cringe? How do you deal with that cringey feeling? Have you just completely gotten over it or do you still feel it sometimes? Well, uh, so, uh, here's a, here's a story kind of that might sound unrelated, but I have, I'm definitely afraid of snakes. Um, I've always been that way. The snakes just, I have a, it's just fun. Whenever I see a snake, I have a heart attack. Whenever I see a stick, I have a heart attack. When I see a rubber on the street, I have a heart attack. I just have heart attacks all the time. Snakes, snakes, snakes. I do not like them, but I once went to a zoo and they said, here's a snake you can hold. It won't bite you. And if it does, don't worry about it. Um, and I was like, okay, I'm going to touch a snake. I'm going to hold it. I'm going to, I'm going to put it in my hands. And I held it with the hope that it would eradicate my fear of snakes. It didn't. Um, I'm still definitely afraid.
And I'll be honest. I do these silly songs at the beginning. And every time I do one, I'm completely embarrassed by myself. Uh, it's, and they never turn out the way I want. I always come up with different words in advance. And then when I'm doing them, everything gets banged, mungled in my head. And I, and I always mess it up and I'm always like, Oh, you know, I'm always regretting that. Uh, but, but I always try to remind myself that, that nobody cares about that stuff. And that's the thing is like, I have to remind myself that it's like, the important thing is that we're having a good time and we're learning something new and, and, and that's, what's important. The details of like, Oh, I wanted it to be this way. And it ended up being slightly different. Doesn't matter. What does matter is like I said, yeah, we're having fun. We're learning. And as long as we're doing those things, it's all those little details of like expectations versus reality. None of them matter. And I just have to remind myself that a million times over and over again.
Career advice: find what isn't being done
Yeah. So I do have I do have career advice. It's not for everyone though. Um, but it's worked for me, which is, um, I always look when, when I, whenever, in every job I've ever had, I've always looked to see what isn't being done that needs to be done. And I've always tried to find those things that nobody's doing that need to be done. And I've done it very successfully in a lot of different, um, avenues in my life. I've done it with music. I grew up playing the cello and I loved the cello and I wanted to be a classical musician, but I wasn't quite good enough for that, but I was more than good enough for rock and roll. And it turned out that the instrument nobody was playing was the cello. And so that was my way of like looking around and seeing what people are doing and saying, well, what are they not doing? Nobody's playing the cello. So I'll start going up to rock bands and say, Hey, do you guys need a cello player? And a lot of them said no, but then one of them said yes. And then we were hugely successful and that was awesome.
Uh, likewise when I worked in the lab, um, one of the things that, you know, I could have just done the math and just given people P values, but instead what I did is I looked around and I said, well, show me how your experiment works. I want to, I want to see how it works and I want to see what's missing. I want to see what, what details you're telling me and what details you're not telling me about how this is done so I can get a better sense of what the data are going to be like. And I've found that's been, that's, that's, that's made me sort of critical on a lot of situations where, um, you know, once, you know, sooner or later the organization catches on that to, to the fact that even though they hired me just to do the data, that I'm actually doing all these other things that are critical to just doing it right. And, and, and getting stuff published and stuff like that, uh, that they're like, Oh, you're very useful. Um, and so I always try to look for the things people aren't saying and the things people aren't doing, um, as a, and then trying to focus on those because it's easier to compete with nobody than it is to compete with everybody.
it's easier to compete with nobody than it is to compete with everybody.
Transferable skills from music
Yeah. Yeah. So one surprising thing is, uh, with the musical talents is, um, yeah, is I, I just like being on stage. I'm a performer. And so I take that attitude when I speak in front of people of, of instead of like holding the microphone all the way down by my belly button and kind of mumbling into it, I get up on the microphone and, and speak in a commanding way because I'm comfortable being on stage in front of audiences. And that's came from being a musician and came from playing in front of people and sort of putting my heart on my sleeve and saying, okay, I hope you like it. Um, and, and that's, that's been useful. The other thing that's very useful about music is it's, um, it's also good for clearing my head. Uh, so if I'm stuck on a problem, I can always pick up the ukulele and strum. It's hard to sing and strum and think about math at the exact same time. Um, and so it's a great way of sort of resetting my brain and starting over. Um, and so I use it as a, as a, as a kind of, instead of like doom scrolling, you know, or something like that, I'll, I'll, I'll, I'll do a little silly song about whatever my problem is or, or even just whatever I had for lunch.
AI as a responsible assistant
Josh, if you were building a stat quest learning system today, how would you integrate AI as a responsible assistant and where would you forbid it to protect true understanding? Yeah. Yeah. So I use, um, I use R all the time, uh, for all of my simulations of P values and things like that to make sure that I understand the math. Um, cause it's just so easy. I, I use, I use Python for doing neural networks. Um, but, uh, but basically I use R for everything, even just like calculating basic things. Um, and I use RStudio and I use Positron almost daily. Both of them. Um, I love notebooks. I, I, in general, I just do everything in notebooks these days, uh, unless it's super basic.
Cause I like Google Colab a lot. I work a lot in Quarto. It depends on what I'm doing and who I'm showing how to do stuff. Yeah. So I do, I, I, yeah, exactly. So on my own, I just do everything in sort of like Positron or even just Jupyter, you know, just to on, on, on, you know, just to have it on myself. But I love, I love, uh, Google Colab because it can, it actually can do R as well. Like I can do Python, I can do R in, in Google Colab and, and I, and then I don't have to worry, you know, it's everything's installed. It's like, I can share it with everybody. And I, to me, that's one of my favorite teaching tools ever. Um, cause it's, it's instantaneous. It just works. You can play with it, you can break it. Uh, and it never touches the original file unless it's, yeah, you can literally just type in a cell, pip install something and it's going to install it if you need it.
So let's get to the question that was anonymous, which was where is AI a beneficial, responsible assistant and where would you forbid it to protect true understanding to make sure people don't, uh, you know, circumnavigate the learning part? Yeah. So I'm a big fan for using anything you can to write, write the software. To me, a lot of the understanding doesn't come from writing the software. The understanding comes from interpreting the results, uh, correctly. Um, and so I, you know, if people want to use AI to write their software, great. That just gets it done faster. I mean, make sure you at least can read it and annotate it, like go back and like annotate or at least read the comments that the AI wrote and make sure it works the way you want it to. Um, but in terms of interpreting the results, I think it's really important to do that yourself. Um, because that's, you know, that's because statistics is the world, the mathematics of the world, because there's variation because nothing's definitive because you're looking at probabilities of like, of ranges of things. Interpretation is really critical and nuanced in a way that AI might be able to do. And I might be able to do, and it might be able to do well, but if you can't do it yourself, then you, you don't understand it. And I, and, and, and if you don't understand it, then it's, I don't think you can confidently make recommendations to your boss or to other people. Um, I think it's critical that you want, you know, that you'd not just, you're not just a parrot for the AI because if you are, you're going to get fired, right? Immediately because the AI is doing your job, right? So you, you have to come up with some way to add value to that. And I think the way you do that is understanding all the nuance involved in the decision-making that statistics requires you to do.
Looking ahead to 2026
The very first one for me was R-squared when I could visualize it and feel it inside. I was like, Oh, I get it. And that was one of my first videos I ever made. We got nine views in the first year, but for me it was transformational.
Oh yeah. I'm excited about so many things. I got my statistics book coming out in February, which I cannot wait for. I'm so excited. Um, but I'm also excited to kind of like break through free a little bit from doing AI and doing lots of videos on all kinds of like time series, getting diving into actually doing time series, but also making videos about getting into getting my hands dirty, but also learning a lot of new things.
I know what a rolling average is. So we'll start there.
Josh, somebody said, is there a pre-order link for your book? Yes, please. But there isn't. So keep, keep tabs on LinkedIn. I'll, I'll publish it as much as I can bear to do. I'm very shy about that, but I'll, I'll try to publicize it.
Thank you so much for coming, Josh. I hope you had a good time. It's wonderful. Thank you very much for inviting me. I had a great time. Oh, we did too. This was super fun. I know everyone, this was not long enough. We needed like a double time hangout. I hope everybody enjoys their holiday. If they're taking a break, don't forget that we do not have a hangout next week or the week of new years, but we will see you back in January when we are going to be joined by Adrian Perez, who is the head of people analytics at GitLab, which is super exciting. GitLab is just like the most amazing open company that shares so many good things about, um, the way that they work with the world. So I'm really excited to have Adrian on. Thank you everybody.