I might ask the same questions every time, but there’s no knowing where the conversations will go! A fabulous episode with the incredible Dr Katherine Ross. Check it out!
“So I think my favorite part about data is that it is completely unpredictable. And that may also be my least favorite part about it.”
“That’s also my favorite and least favorite thing about coding, my favorite thing is that coding does exactly what you tell it to do. And my least favorite thing is that coding does exactly what you tell it to do.”
“there’s no fundamental truth that’s buried in data. I think we all want there to be. We all assume that there could be. But I just don’t think that’s the case. I think data is a representation that combines the observer, the methods to collect it, as well as the things that are actually going on. And there usually are multiple things that are going on.”
‘And inevitably it’s going to happen. Everyone will have that moment. It even happened to Nobel Prize-winning scientists who put out an international telegram to say they found a fancy new transient, something that’s popped up in the sky that wasn’t there. And it was Mars and then they had to come back being like, “Just kidding. Never mind. Don’t worry about that one actually.” So everyone! happens to all of us.’
Transcript
Linda: Welcome back to another episode of Make Me Dada Literate. I’m very excited about this one. I think we’re going to roam far and wide, just like the last one. And it’s going to be a real cracker. So thank you so much for coming. Welcome, Dr. Kat Ross.
Kat: Hi. Thank you so much for having me.
Linda: I’m really excited. So the starting question, who are you and what do you do?
Kat: So as you said, my name is Dr. Kat Ross. I use she/they pronouns, and I’m an astrophysicist. So I work for the Australian SKA Regional Centre, and my role is as a support scientist. So everything I do is to help bring science possibilities to the scientist as we build the biggest, best, most incredible telescope in the world in the history of astronomy. So it’s a big global project building this telescope with levels of data that we’ve never encountered before. So some of the biggest challenges that we’ve ever faced in astronomy. And I come from the science perspective of I want to do all the science. I want to hoard all the data. I want to keep everything. And I help to bridge that gap between the scientists like me and the software engineers, the engineers building the telescope, and actually make sure that as much science is possible without completely overblowing the amount of data that we have.
Linda: That’s awesome. I know with the SK~AO, you just can’t store it all. It’s not physically possible.
Kat: We can’t. And it’s a really hard lesson to learn as the astronomer because even in the last couple of years, we’ve made discoveries in the archives of data from telescopes. So they’ve just been sitting there for 30 years, and we haven’t known it was there, and then we make this discovery with the archives and see the history of this object. And now we’re just facing a whole new world where we basically take the observations and we have to get as much science as we can from it before we delete it. But we don’t know what science we don’t know. So how do we know that we got all the science from it? That’s why I want to hoard all my data, please.
Linda: That’s such a good point. I mean, when you look at things like the story of functional MRIs where it turned out that the first 15 years of using them, the algorithms were quite broken and reporting false results. And they actually got brain activity out of a dead salmon. So you couldn’t go back and edit those because they only had the results, not the original scans. And so you worry that you’re throwing away the good stuff because you don’t know how to detect the good stuff yet.
Kat: We don’t know what we need to keep. Yeah, it’s a really big discussion of how do we keep something that’s flexible enough that if we make a discovery later on that we need to do more analysis, we can, but not something that’s so flexible that we’re just keeping all the raw data because we physically can’t do that. To put it in perspective, some of the estimates for the amount of data that will come through when the entire SKA is online, we’re expecting volumes that are comparable to the entire internet every day coming through that telescope. So it’s just not feasible to keep that level of data every single day.
Linda: But also there’s this, oh my God, what’s in there?
Kat: So much data. It’s amazing.
Linda: Ah, that’s fun. So what did you have to learn to do what you’re doing now? Was there anything missing from your formal education?
Kat: Oh definitely. I did a Bachelor of Science with a double major in mathematics and physics. The mathematics was actually accidental. So I always wanted to do the physics, but it was definitely astrophysics. And I love maths. So just any extra subject that I had was always filled with maths. So when I graduated with a double major, it was a pleasant surprise. But I definitely learned along the way there were a lot of things that would have been useful to have studied that in undergrad, but I didn’t.
So I remember in my first year of undergraduate, I had these extra courses that already taken all the maths that I could, already taken all the physics, and I was like, what else now? And I remember my dad saying, oh, maybe take some sort of coding or software engineering or something like that. And I was like, I’m doing physics. I won’t need that. That’s a waste of time. So that was one of the bigger mistakes of my life to not take any kind of programming.
So then all the programming I learned was sort of along the way, doing it on the spot as part of other projects and never actually formally learning to code. And then I got to my honours year, sort of extra year to latch on to my undergraduate. And I did an entire honours research project on simulations of dust grains around a star, which requires a significant amount of coding.
Linda: All coding all the time.
Kat: Yeah, we went from nothing to thrown into the deep end, simulate these dust grains.
Linda: Oh boy.
Kat: Yeah, it was certainly one way to learn. Not sure I would recommend it as the best way to learn. But it got the job done. I learned. Trial by fire.
Linda: What was the, what were you simulating in? I was using MATLAB. And I used MATLAB mainly because it was the only language that I had any knowledge of. It was the only one that we did in undergrad as part of our physics labs. We were doing some stuff in MATLAB, but it was never actually, we were taught to code in MATLAB, but I honestly didn’t even know what else was out there. I don’t think I’d even heard of Python. And then I finished and everyone was like, why did you do this in MATLAB? I was like, what else is there? Is there another option? And I would have had a significantly faster route if I had done something in Python … anyway.
Linda: That’s kind of one of the dirty secrets of science. A lot of things happen the way they happen only because that’s what the researcher knew or that’s what the student’s superviser new or like that’s what happened to be the dominant package in the lab that they were working in. That sort of, it’s not necessarily what is the best tool for the job. It’s like, what do I have to hand that’ll work?
Kat: Yeah, you’re absolutely right. IDL was actually a language I also used in my honors. And that was because I was simulating these dust grains to match an observation. And the processing of those observations was using a pipeline that was written in IDL. The only reason it was in IDL was because exactly my supervisor was familiar with it. But IDL you can only get with a private license. It’s not really one that you have public access to. So I had no experience with it. I didn’t know anything about it. There was also very little support online for it. And half the reason it even still exists is because there are enough pipelines around that rely on IDL and it’s more effort to convert them to a different language than it is to just keep IDL around and let them keep working. So I had also used IDL.
And so I came out of my honors just being like, I know how to code in these things. And everyone was like, that’s the most unhelpful skill. No one will need this. Yeah. So even when I did learn to code, it wasn’t necessarily transferable straight away to the coding that I do now. Because yeah, so much of it was just done by default of what was around.
Linda: Yeah. Yeah. Did you learn any data skills in the course of your degree?
Kat: Not really. Not in my degree. I took a course of data science in my honors year. But that was really the first time that I’d done any formal data science stuff. A lot of it was really, in my PhD, of sort of playing catch up. And thankfully there’s people in my institute, for example, this is a very new emerging field. And I would argue there are some people that still don’t think it’s actually a field yet because there’s just not enough people that do it. But we have like bio statistics of we realize the information we get from biology fields, we need proper statisticians that are also familiar with biology to understand and interpret and create the statistics necessary. And astronomy is kind of rapidly realizing that that’s a severe missing part of what we have.
So astro statistics is this sort of ever growing field. And so I was very lucky that in my institute we have an astro statistician around who could actually help me interpret a lot of this data. I went from studying and simulating the dust grains around one single star. It’s actually in the northern hemisphere. So I spent my entire honors monitoring this one star that I had never actually seen with my own eyes.
Linda; Oh that’s mean!
Kat: It was so heartbreaking! People would be like, which star is yours? And I’m like, I can’t even show it to you. It’s not here. You can’t see it here. So I don’t say anything. And then I went from the dust grains around this single star to studying surveys of hundreds of thousands of radio galaxies. So the volume of data changed significantly rather than delving into the itty bitty details of a very specific object. It was you have ginormous populations. How do you draw any kind of conclusions on those populations? And coming from a very little statistics background, very little data science background, being able to churn through that data and actually get meanings from it and interpret things. It was a brand new skill to me. And once again, I, you know, a trial by fire, I guess, it seems to be a common theme, learned on the job, figured it out as I went.
Linda: That’s, that’s been the story of, I think, almost everyone I’ve had on the podcast. I mean, oh, hell yeah, I had to figure that out on the go. Like, because I couldn’t do it otherwise.
Kat: Exactly. And I had no idea that that’s where I was going to be either. I mean, astronomy. Yes, there are things that haven’t changed for billions of years, but it is also a rapidly changing field in a lot of ways. So the objects that I study now, there were things that I study and the way that I study them, the telescopes that I use, a lot of them weren’t around when I would have been making these decisions. So there’s no way for me to predict that. And I think it’s, in some ways, a good thing of like, you can’t really choose wrong because how would you know, but in some ways it’s frustrating because you’re like, well, how do I prepare? I can only ever learn on the job, I guess.
But I personally like it because it means I always get to learn something new. And I find it much more interesting to learn this new thing when it’s for something interesting. Stats was always really boring. But the moment I’m doing statistics on my little baby black holes, I’m suddenly very interested in figuring out the correct statistic.
Linda: That’s, I mean, that’s, that’s so much the message that I keep hammering in all of my work, which is there is no point in asking kids to learn something that they can’t see the point in. You’ve got to make it real. You’ve got to make it meaningful.
Kat: I don’t care about a bag full of red and blue balls. No. Oh, man. That’s boring. So boring.
Linda: That’s, yeah, that’s my pet peeve. I went through the math methods curriculum recently for the senior secondary and all of the probability stuff was balls in and like, what are you doing? There are so many more interesting things.
Kat: Yeah, you can still take the same thing, but make it interesting or give various options and let the teachers be like, actually, my class is particularly interested in this. We’re going to teach the same mathematics, the same statistics. But in this scenario, something that’s far more interesting.
Linda: Yeah. Yeah. 100%. Is there one thing that you wish everyone knew about data? One thing where you go, man, if everyone understood this, would be so much better.
Kat: I think, and it’s certainly the case I find particularly in an area like physics, which kind of has this perception of being something that’s so rigorous and fundamental. And we’re always trying to find this fundamental understanding of the universe. So there is this truth that’s there and we’re sifting through the data to find that truth or to understand that truth. I think that’s kind of the view that we have of what physics is and particularly astronomy. There are these rules and laws of how things work. And when we take these observations, we’re trying to find what that rule and law is that’s dictating the motion of these galaxies or these atoms around in the star, whatever it might be.
And I think that gives the perception that when we take this data, the story is there and we’re using the maths and the data science to sift through and find it. When actually, I think we need to change that view that there are multiple stories intertwining in this data. And it is about understanding how to unravel them as much as you can, but also knowing that anyone can kind of create the story that they want in that data. You can work with the data because you have so many intertwining stories.
There isn’t this one fundamental truth that you’re kind of honing in on. In fact what you’re trying to do is understand the complexity of different narratives, different stories, different truths and how they interact together to create one final outcome, Which is where when we look at the universe, every single galaxy is unique. They are all their own little snowflake, everyone is different. But that means when we’re trying to draw conclusions of how the galaxies evolve, you’re looking at these broad strokes populations. You’re looking at these huge volumes of data.
For example, as I said, I worked on hundreds of thousands of galaxies and I sifted through the hundreds and thousands, these catalogues of galaxies to try and start to build an idea of how galaxies would evolve and how they change over what time scales and what their sort of evolutionary stages are. But every galaxy, you change one of the many tens of thousands, if not millions of parameters for that galaxy and you suddenly get a completely different outcome.
So you have to gloss over and pick which of those truths, which of those stories, is most relevant to what you’re trying to determine. And so as a result, we have this perception that physics is so fundamental, that it is so unbiased, that it is such a fundamental truth. When in actuality, the scientists themselves can put so much of their bias in there, so much of their perception, just in how they’re analyzing that data and what they’re picking out. And I think people have that realisation for other areas of science, but because, and this is, I could go into the social concepts behind this, and the superiority that has embedded in Physics as a result of this over hundreds of years. But because Physics as grown as being this superiority, we’re above everyone, fundamental truth, I think there’s the view that those other fields of science are less pure because those the scientists can impact the story that they’re telling, but the same is VERY very much true for Physics, and for astronomy as well.
So when you see these results coming out, always, no matter the field, no matter how obvious it may seem, there needs to be that grain of there’s some level of scientific input in this. There’s some level that a person has done this science. And even if it’s not a person, if it’s machine learning or AI, a person has written the machine learning and the AI that has done this analysis. So no matter how it is done, how it is analysed, and how you sift through the data, there is human intervention and perception that is embedded in that interpretation,
Linda: So people who are listening to this podcast in the order it comes out are going to be going “Did she put you up to that?” because it’s exactly what Emily Kahl said in the last episode, which I just edited today. It’s like… the same ideas – that we all bring our context and our bias, and our understanding and our experience, to the questions that we ask. And you have this mass of data and it’s not like just looking at it and going “oh! There’s a truth, there’s a truth, there’s a truth.”
Kat: I found the thing! And it’s undeniable!
Linda: You have to ask a question. You have to look for stuff. You have to know what to look for. If you just have a big mass of data, there’s no knowing whether there’s anything worth looking for, and there’s no knowing what’s worth looking for, without having to come up with these questions, and figure out what to go looking for.
Kat: Exactly. And fundamentally in astronomy, as much as I’m talking about the petabytes and petabytes of data that we’ll be generating daily, so yes, they’re volumes of data that are kind of unfathomable, that we’re processing, but regardless of the volume of data no matter how much data we take how many observations we take there’s always fundamentally going to be gaps in that data we take. We cannot observe every galaxy from the birth of the galaxy to the death of the galaxy. We are not there that entire time. We are drawing conclusions that are filling those gaps. So the scientific method the process of science is trying to infer those gaps and understand that narrative, given the data that you have. But because, again you’re trying to search for something that is a fundamental truth, but that is intertwined with so many things, and then you’re taking great chunks out of it, so that you’ve got this unfinished puzzle of something that you’re not even sure what the final picture is meant to look like. And then you’re trying to say “that piece potentially looks like this.”
And so you’re drawing so many conclusions with incomplete data and you’re just trying to argue the case as best you can. And so we do a lot of work to try and mitigate the biases that we have, but I think that physics has a long way to go, and I think we’re quite far behind many other areas of science, in that we mitigate our biases from things like telescopes, the things that are measurable, but we assume the scientist themselves is completely unbiased, where I think many other areas of science recognise the scientist has a big input here, and we need to account for that, or at least try and mention it where possible. But Physics seems to be well behind the curve in that it assumes the scientist is perfect in every way. We would NEVER introduce any kind of bias. Noooo.
Linda: We’re scientists, we’re rational thinkers. There’s no emotion here!
Kat: Exactly, I’m purely logical, there is no emotion in this whatsoever. It’s a dangerous perception to hold as well, because I think for example that kind of mentality that scientists are logical and pure and away from thought is where you get things like colonisation. This is where we had the kind of justification of serious atrocities across the world. “Because we need to travel to Australia to observe the transit of venus.” And therefore I am justified in whatever I do for that need, because logically these are the observations that I need, and whatever the means to get there, that’s justified, And so the removing of that emotional side and the perception that the emotion has no input in the science, is also a very dangerous route to take as well. I think it’s important that we always remember that scientists are a part of society, and therefore also have all the biases that society has. Even if it seems like the science is so irrelevant to society. Because it never is.
Linda: Yeah, that’s such an important point to make. And it’s one of the issues that I have, quite apart from readability, with the whole passive voice removal of the scientist from the writing about the science kind of thing. So you don’t say, we did a thing or I did a thing. You say, a thing was done because the scientist is not a part of the thing. And that’s not true. That has never been true.
Kat: Science occurred. Scientists weren’t here. Yeah. Not in the what happened. Well, okay, I will say, you know, the galaxy did evolve whether or not I saw it happen. But it’s the, how I observed it. Definitely. Scientists were involved. How we chose to take those observations. How we process them.
Linda: Yeah. Yeah. I like that. I think that’s the first time anyone has said that about, you know, sort of that question. And I think it’s absolutely one of the fundamental truths that we kind of have tried to sneak under the rug and pretend isn’t there. That people are involved and people are emotional and biased and complicated and influenced by all kinds of things. And, you know, we tend to believe what’s gone before, which is also potentially dangerous.
Kat: Yeah. And I think like that comes from the, the history of sort of the Institute of science and where science came from, you know, when we look at modern academic science today, it came from a very Eurocentric Western model, which that was a fundamental part of, you know, being a man of science that you were above society that you were, like this was embedded in how we developed science in the modern world. And it’s also where we lost a lot of things like indigenous science and we lost the indigenous ways of doing things and, and knowledge keeping in that way, because it was fundamentally believed that this approach was far better, was far superior.
And so it is at the core of science in a modern world that scientists are independent. And I think it is important to in some ways remove yourself, but you also never can. And also, I mean, I know certainly for a lot of the research that I do, the experiences and the history that I have, me as the scientist, has changed how I approach things and has adapted the ways that I want to do my science. Even some, some little things like when we’re making the final image in astronomy, at least for radio, there’s a, it’s a multi-step process where you kind of create a first-pass image and you say, there is a little source here, there’s something right there and you draw little boxes, you know, here are where all my little galaxies and now do another round and clean the image a little bit further knowing that these galaxies are here.
And we’re improving algorithms that can automatically detect, oh, I know that there’s, you know, a galaxy there, a galaxy there. But I personally, in my PhD, when I was processing, I found it really hard to remember. I mean, I have ADHD, so if that wasn’t obvious already, I often get distracted. So if I click, go on something to process and run this imaging with an automatic algorithm, I would forget about it, wouldn’t come back to doing it. But if I have that interactive cleaning of, do a little bit, come back to me, I can constantly engage with, with the image and it just, it was far more efficient for me to be involved in that processing.
But it also actually meant that there was fainter, softer emission that was quite, wasn’t these little bright dots, but more these, these big diffuse structures that are really hard for algorithms to pick up, we’ve actually not got a good system for detecting that yet. So by me being involved in the cleaning, that cleaning actually turned out as a better scientific result. So the experience of the scientist as well, working on pure efficiency and removing the input of the scientist can actually reduce the science quality also.
So there’s a, it’s a very, very difficult thing to be assuming having this, this idea that scientists should be removed and all the emotion should be removed. It’s not actually necessarily the case in any science.
Linda: There’s always the need for creativity and insight and we, you know, for all of our technological advancement, we haven’t produced systems that can have insight or be creative yet regardless of what Open AI may claim. It’s, you know, it’s a nice, that’s a nice little kind of illustrative example of why we can’t just, one of the reasons why we can’t just replace everybody with AI because it’s not there. It doesn’t have that capability.
Kat: And we’ve been trying for many years in astronomy to bring up machine learning to do a lot of these things, to identify the galaxies, to, you know, classify the stage, the evolution, the type of galaxy, but there is nothing as efficient as a human brain at pattern recognition and also the detecting these sorts of structures. And once you have that expertise look on it, it is so fast and quick and you have such a broad expertise to draw on every time you’re looking at these images, that machine learning will just never be able to replicate no matter how much more advanced it becomes.
Linda: Yeah. And machine learning is really good at very small, very specific, very discrete tasks. But so you can, you can get a system that’s exceptionally good at detecting, say, lung cancer. But if there also happens to be another thing that shows up on the scan, the machine learning system will completely ignore that. You know, if it, it says, no, there’s no lung cancer here, but the scan also includes part of the thyroid and there’s a very obvious cancer in the thyroid. Machine learning is not going to report that. So if you never get a radiologist look at it, then you’ve missed something really important.
Kat: Exactly.
Linda: Yeah. What are the worst data mistakes you’ve seen?
Kat: Oh, gosh. There’s plenty to draw on, I feel. In astrophysics in particular, I think it’s quite common. So let me back up a bit, I guess, first. So I do a lot of outreach. I do a lot of talks to schools, to general public. And part of the reason I do that is, A, because I love talking about space, but B, I think space is one of those things that everyone is excited by. Everyone loves to hear about space. I get to say, you know, I found a black hole that burped 700 years ago and everyone’s like, no, that’s amazing. No one sort of questions what you’re studying. It’s all just like, I love this. Whereas, you know, I think climate scientists and biologists that are saying, like, please keep your cat in doors. And everyone’s like, oh, what would you know? My cat would never. And there’s a lot more pushback. So it’s one of the great things I like, talking about space.
As a result, though, there’s always a lot of interest in space results and anything astronomy, anything that we’re doing. The media is always excited. The general public is excited. Space is just a thing that everyone can kind of get behind as being something that’s really cool. So as a result, I think it’s really common for astronomers to over stretch what their data can actually say. And they make a lot of claims that you do not have the data to actually make that claim. You could hint that this is a possibility, but it is one of, let’s say, a dozen possibilities. And in fact, there are far more likely possibilities than this. And I think one of the examples of this, and I mean, I don’t know if I can name and shame. I’ll keep them as anonymous.
There is a particularly well-known outspoken astronomer who says they found evidence for aliens, these alien structures, because we saw a rock, a meteoroid, flying through the solar system. And the velocity of that rock and the trajectory that it had, the path that it had, they claim that that means it’s sent from aliens in a different solar system. And that could be an explanation. Sure, however, Occam’s razor, the simplest answer is probably the right answer. And there are so many simple solutions to that. there is a lot of space debris out there. There’s a lot of meteoroids. And it could, in fact, be easily explained by general trajectory and being a sort of flying meteoroid. It was a weird shape, sure, but there’s a lot of weird shapes in space. It’s a big place. There’s a lot of things there.
So to jump straight from, we’ve found something weird, it must be aliens. We are all so keen for things to be aliens. I would love for anything that I observed to be aliens. That would obviously be amazing. But you have to do your due diligence first. And if you can explain it with something else, it’s probably going to be that.
And so I think the want for immediate attention can often outweigh the science of… It’s probably not as interesting as we think it is, sadly. It’s still interesting, but probably not alien.
Linda: Yeah, it’s that key tenet of science, really, isn’t it? That you have to throw everything you can at your theory to try to disprove it. You’re not, or you shouldn’t be, setting out to prove your theory. You should be setting out to disprove it. And we forget that. We get so excited. We’re like, “Oh, I just want to confirm what I think I found.” No, you should be trying to prove it wrong. Because if you’ve tried your hardest…
Kat: If you can try your hardest and it withstands all of that, then it’s the continuing theory, absolutely. But it’s the science is done, not just by scientists, the individual who are part of society, but also science done by scientists at large, whole groups, collaborations. The whole point of this is that you publish your results, you argue them, you see other people pushing back on them, and you ultimately work towards the concluding theory that explains what you see. So it’s a very natural part of science for you to present results and immediately have someone publish a paper a week later saying, “Well, that’s wrong. Here’s why.” And I think that’s a really great part of science. A, I love the gossip. I love seeing the back and forth drama. I often really enjoy it. But B, it is an important step of that. And it’s when taken out of context, for example, this alien scientist of, “Well, everyone is trying to shut me down for this theory.” And it’s like, “No, this is actually a valid part of science.”
This discussion is actually really, really an important part of this debate stage where we reach that sort of most likely conclusion. And even then, it’s never, we’ve definitely found aliens, we’ve definitely explained this thing. It’s, we have a conclusion that fits the data the best with the information that we currently have. This is the most likely scenario. And there are several cases in astronomy where there’s been this sort of back and forth.
We actually recently had a talk from a student here who’s giving a background, intermediate mass black holes. They’re sort of theorised they should be there, but we just, we’ve not found a single one. We’ve been looking for decades to try and find these very specific ranges of how massive these black holes are, and we’ve just not found any. And in particular, we’ve not found them in these particular types of clusters of stars. So this student was given the overview saying, “I’m looking for these black holes in this particular cluster. And this is my method for doing this.” And you may ask, “Have we already found some? Have there been attempts?” And they just go through this back and forth of, you know, in the ’60s, someone said they found it, and then ’65, someone said, “No, you didn’t.” And then we went back and we did find it, and then, “No, we did not find it.” And then someone came back saying, “No, but this could be it.” And then the next person, “No, but you didn’t find it.”
And so there’s just a dozen back and forth of, “Yes, no, yes, no.” Which I just, I love seeing that narrative as well of, we look back at some of the things that we used to believe and you’re like, “How do we think that? That was so silly of us.” But then, you know, someone will think that about what we’re thinking at the moment.
Linda: That openness to, “But I could be wrong and I’m going to examine everything that comes in and go, ‘Ah, yeah, that, okay, so this shows that I’m definitely wrong.'” Or, “Ah, okay, you know, we’re still in with a, you know, with a chance here. This is, you know, doesn’t, this doesn’t conflict with the hypothesis that we got going.” That’s really good science, but that whole human…
Kat: It’s an exciting part.
Linda: Right? Because you’ve just learned something. You just discovered something new. And what you’ve discovered might be that you were wrong, but that’s, like, that’s good. We need this whole new attitude to being wrong, that, that, you know, finding out that your work is flawed, finding out that there are problems with it, finding out that it’s outright wrong. That’s the good stuff. Like, that’s where we make progress.
Kat: Exactly. One of my favorite parts about my job, and this was a big part of my PhD, in particular, I found something. I was studying baby black holes and, and we expected, even though they’re babies, we call them babies, they’re still, you know, babies on astronomical scales. So they’re still 30,000 light years across and ginormous. So we didn’t expect them to change unless you watch them for about 30,000 years, which is obviously much longer than my three year PhD. So everyone was sort of like, they won’t change. We’ll just kind of check out on them a year later and make sure our data processing is fine. And so I was doing that. And then I found actually they were all changing. Everyone was doing crazy things. They were all over the shop.
And I presented this just being like, everyone’s doing a bunch of different things. And people were like, oh, but they, they can’t do that. And I was like, well, they have. So it’s actually not about whether or not they can, because they have. So now it’s about explaining this. And so I loved that it’s a big part of my job to just, I would have, you know, the plots up my observations up on my screen and there would be a time. So it’s like a full hour that I’m just sitting there staring at my screen and just coming up with what could explain what I’ve seen. Like what if I change this one thing in that theory, what would that look like here? And could that explain what I see?
And that is the best part where you just get to sit and try and explain something. You get to just wildly speculate and come up with something that could explain this. So when someone comes out with a paper that says, no, everything that you’ve done is wrong. I completely disagree with you. That theory doesn’t make sense anymore. Or even like yourself, I have a theory. I take more observations and now those observations have completely disproven the thing that I was setting out to prove. And so you get to sit there and now again, wildly speculate with new information and come up with something new that can explain this. And I think that’s the best part of it.
So to sit there being like, no, I’m going to keep believing in this theory and take away the best part of my job just so I can claim that I’m going to be able to do something. Just so I can claim that I was right the whole time is so upsetting and bad science. So it’s the same argument with Pluto. I think people were so emotionally hurt when astronomers decided to reclassify Pluto as a dwarf planet. But that was a moment where the entire community sat there and was like, not only is Pluto not a planet. We can see how all the planets they fit together. They’re all act really similar. There’s a lot of properties that they hold that are all very consistent and Pluto doesn’t match any one of those sorts of things. It has a lot in common, but it is really the oddball here. And that’s fine.
You can have an oddball, no worries. But it was the moment where we’ve gone, it doesn’t fit here, but there’s actually we’ve discovered a whole new class of objects. There’s actually so much more out there. So this reclassification, it wasn’t a demotion of “it’s no longer a planet. It’s this other sad thing”. It was a recognition of “there’s actually so much more out there that we accept we don’t understand. And we’re going to start looking at those things that we don’t understand. Let’s try and understand what they are. Let’s try and get more information.” So this moment of emotionally sad that, yeah, Pluto, I’ve always thought of as a planet and now it’s not. And I have to get used to that.
But shifting your focus from instead of saying, I want more planets, more of these things that I already know about. Why would you not want more of a whole new thing that we don’t know anything about and we get to learn about? It’s just changing your perspective on – you’re not necessarily wrong. You’re learning more things.
Linda: Yeah. Yeah. And that’s that’s something I try to put into the projects that I write and the teacher training that I do too is the idea that getting things wrong is great because it means you’re learning stuff and you’re progressing. Absolutely. My son’s driving teacher was amazing. Every time Sol made a mistake, he’d be like, Oh, that’s amazing. Like now you know that thing and you won’t do it in the test. That’s fantastic. Such a good attitude to making mistakes like it is fabulous. Now you can get past that one. You know, it’s brilliant.
Kat: Yeah. Because also how would you know to point those things out unless you make the mistake unless the mistake happens? You don’t know that it was a mistake necessarily.
Linda: Yeah. It’s an important step.
Kat: Yeah. I can’t even remember what the question was. I’ve rambled on a whole different direction of whatever we’ve started out as.
Linda: That’s actually really normal on this podcast. Have you ever seen data deliberately misused and what do you look for? How do you spot it?
Kat: Yeah, absolutely. I think it is also one of the things once you are a scientist and you start to notice the important things even just in your undergraduate, the things that sort of drill into you in an undergraduate science degree. It becomes very clear when that’s not being done for media things, for example.
So some of the biggest things I always look out for: any graph that’s being reported in the media, what are on the axes? If there are no units on the axes, I will not take any graph seriously. If there’s no label on the axes, I also won’t take it seriously. Having just a histogram and saying this bar is bigger than that bar. If I don’t know what the bars are, I don’t know. I’m not sure if it’s a 2 and a 1. Yeah, the 2 is bigger than the 1, but if it’s on a scale out of 100, okay, it’s not that big. If it’s on a scale out of 3, yeah, okay, I might be a little more concerned.
So a scale having proper units, proper description on your graph is a big thing. And also having references. So this is a big thing of even just having the references there is a good sign that I’ve taken this data from this study. But Then having the knowledge of the difference between a study and just a journal article, something that was published in the paper. that is still a source, yes. But if I can’t go myself to read that research to see how did they do this study? Did they follow the steps correctly?
So I’ve personally read many research papers that were well outside my area of expertise. And I’ve not necessarily understood the nuance of this molecule that they’re researching or things like that. But being able to study knowing a good scientific method and knowing the information that’s provided in a research paper to see, do I trust the results that they’re saying here? And also can I trust the claims that they’re making? Does it seem like they have sufficient data in that paper to make the claim that they’re making? And then going, does that claim match what the media is claiming?
So there are a lot of times we see papers where the scientist has done everything correctly, has said, we’ve done this analysis, the data is of this quality, this standard, it’s not a huge population. But from this – potentially – we can draw these conclusions. What we need is these observations to help figure out if that is true. And the media will come saying, this is 100% the way we interpret this. There’s no wiggle room. So I think the wiggle room scientists are usually very good at including wiggle room in there. We’re very good at saying, this is not necessarily the complete truth. This is what best explains what I have. I say, as we’ve mentioned, that’s not necessarily the case all the time.
So I think if you’re seeing things in the media where it’s saying, A, just scientists without saying which scientists, where, who, just scientists have shown that this thing does this thing. And it’s all very 100% clear cut. That sort of phrasing is already quite of a red flag. But as I said, scientists also often will make these mistakes as well and misuse data. And it’s picking and choosing the data to support your claim.
As I mentioned, sort of right at the beginning, there’s no fundamental truth that’s buried in data. I think we all want there to be. We all assume that there could be. But I just don’t think that’s the case. I think data is a representation that combines the observer, the methods to collect it, as well as the things that are actually going on. And there usually are multiple things that are going on.
So there’s a whole bunch of interweaving factors at play that if you are, you are trying to support your hypothesis and you sift away to only have the data that supports your hypothesis. That is a very, very dangerous thing to do. And it’s, I think, very common, particularly for people with certain agendas who want to, A, get funding or B, promote some ideology that they may have, which is a really scary, scary thing. When people are not necessarily as data literate to know that that’s being manipulated in that way.
Linda: Yeah. But it’s also super easy to fall into that trap accidentally when you’re presenting stuff. Exactly, yeah. In fact, I was presenting a workshop on urban heat islands with a friend who is a scientist. And he said, you know, and so here’s the temperature in a rural location on this one day and here’s the temperature at the same time in the city. And he went “So you can see that the temperature in rural areas is always lower than it is in the city.” I see you rear back and make a face.
Kat: Mmm that’s a bold claim!
Linda: And I said to him, did you really just take one day and say, and that shows always.
Kat: One day, two data points, and therefore conclusion.
Linda: He may have kind of discreetly given me the finger. But he then was like, that’s a really good point. And we should, you know, we have to be careful. And, you know, the kids can see that, you know, even scientists do this and, you know, we have to watch our language and think about the conclusions that we’re drawing and ask those critical questions. So the fact that he took that in his stride and was like, oh, heck, yeah, I shouldn’t have. Whoops, you know, but it’s so easy to do because you know that, you know, the evidence is really strong. And to fall into this, this proves trap is just like, I think, I think we crave certainty. And so we want to go see, you know, I told you.
Kat: And I’ve found, yeah, I found in my experience as well, particularly as you become more specialised in certain areas. But also, the further I’ve gotten in my career, there have been times where I, you kind of make conclusions that you don’t necessarily have the data or the knowledge of where you got that conclusion from. And that does come because your brain is so great at that kind of recognition. And I think ADHD brains in particular are really great at drawing these conclusions from seemingly irrelevant things to everyone else. And so in those ways, I do think that helps how I approach science.
But it has often, this has happened on multiple occasions where a paper will come out and it’ll be like, we’ve made this, this wild connection between this feature of a galaxy and this property. And I sit there and I’m like, I thought we knew that from, from years and years ago because it was such a default thing that I knew. And so the scientists, when you have this vast knowledge, you know, you have so much niche expertise in such a small area of the universe or the world, and you can draw those conclusions, you can make those connections. But often you don’t even necessarily realise that you have made those connections as well. And it can be good in that you, you know, you’re drawing these conclusions, you’re making connections on different things and studying the universe in this new way. But it is a dangerous thing of if you don’t necessarily realise that. And sometimes they are really obvious of, yeah, you know, country towns or the outback is always cool or always hot or I can’t even remember what this person said, but it can be so obvious to you. And it might be true. But unless you have the data to make that claim, it is obviously not necessarily something that you should be banking all of your assumptions on and your entire model. If it breaks down that that assumption doesn’t hold true and then your entire model falls apart, you should definitely make sure that that thing is, it’s true.
Linda: Yeah, it’s just that kind of fundamental thing of you’re always… new information is always coming in. And so then you check your model against the new information and you’re like, ah, so this doesn’t fit, which means something about my model is wrong or something about the measurement was wrong. How do we, you know, check both of those things?
Kat: Yeah, exactly.
Linda: They could both be true. You know, sometimes you get data that’s serious because the telescope wasn’t well shielded and someone turned the microwave on, you know, like,
Kat: It happens to the best of us, you know, happens to the best of us. And like, I think it’s often described as a rite of passage in radio astronomy and I certainly did it in my in my PhD and I’ve also done it in my postdoc, of discovering something again, basically, and I did a bit of a video on this recently on on tiktok and Instagram. So in my PhD, as I said, I was telling these weirdly changing baby black holes that no one said could change and then they were. So I developed an observing strategy to try and monitor them and see if they were still doing this and get more information on how they were changing so we could try and figure out what was causing the changes.
So I monitor these, these same galaxies multiple times throughout the year in lots of different colors and getting as much information as I could. And this was where I found a very weird thing in my images. It was something unexpected and it is that moment of as a scientist. So I found something cool. It’s the greatest thing in the world. Amazing. I’ve changed the game. But you have to as a scientist be like, this is almost certainly not the cool thing. This is probably something boring. Let’s not get excited and going through doing your due diligence to make sure that it is not something boring before you make a claim that it’s not exciting.
And so I spent a long time trying to figure out what this was and it had popped up in multiple images, even though I was looking at different places. So it’s the same coordinates. So it wasn’t something like a satellite that was, you know, zooming through and it was there for that entire observing night. So definitely not something that just popped up. But I looked at catalogs from the past with the same telescope and there was nothing there.
So, you know, I’m doing all of these checks trying to figure out what it could be and asking around. I wasn’t shy about the fact that I found something. It’s surely not that interesting, but I don’t know what it is. Let’s figure it out. And it was such a mystery. No one could figure it out until I figured, OK, I don’t know what this is. It’s something really cool. Obviously, it’s this big bright thing that’s a pin in my observations. What I’ll do is I’ll take a note of the observation. I’ll finish doing the science I am doing, but I’m going to come back and figure out what this is. And as I went to the observer page with that date of observations, they give you a snapshot of what the sky was like at the moment of that observing, which is when I learned that it was the sun.
Linda: Oh, no. But it wasn’t just you. It was everybody you’d shown it to.
Kat: Everyone. That’s it. It’s one of the great things about Radio Astronomy we can observe during the day. But it does mean that we often forget that sun exists and that daytime is relevant because we’re just observing it’s fine. And so I discovered the sun and then I had to go back to all the scientists being like, well, I figured it out. It isn’t exciting as we all thought it was indeed something that that was just, you know, boring. But I’m glad we sorted it out. Please don’t ask me any further details.
But it’s a very big rite of passage because that whole process of everything in you wants to be like, I have just discovered something that’s like the brightest thing in the sky that wasn’t here a year ago. And now it’s here. This is so exciting. And you want to get on the exciting train. Like that’s what’s so cool. But you have to sit there and be like, but it’s probably something boring. Let me just rule out the boring first. And so far, 99% of the time it has been something boring.
I’ve heard just in my institute alone, we’ve had discovery of the moon, the sun, the galaxy, planes, satellites, well known galaxies. Yeah, we’ve done it all.
Linda: That’s fantastic. And I love that you tell those stories because, you know, the process of science is often thought of and described as really quite linear. You know, you did the experiment, you did the work, you did the analysis, you came up with, ta-da, amazing thing. You know, like, but actually so much of science is a happy accident or an unhappy accident in the case of those stories.
Kat: And I just think it’s hard as a scientist when that’s only narrative that you get because all you see is that these scientists have gone from start to finish knowing the entire route, knowing everything that they were going to do. They knew basically the result they were going to get. They set up observations, they processed those observations and they did get the result that they wanted to get. Like, that’s, we assume that that’s what it is. So when you deviate from that in any way, or you make what we would call, you know, a stupid mistake of forgetting the sun exists.
So it can be so demoralizing and particularly for anyone who is already struggling to feel like they fit into that environment. Like, I already struggled so much with imposter syndrome and just crippling self-doubt of my worthiness of being in such an institute and such a field to then make this sort of discovery and have to go back to these world-leading experts in radio astronomy and be like, “It’s the sun. I didn’t check the sun. I checked everything else and I forgot about the sun.” It can be so hard to be like, “They must look at me and be like, ‘What an idiot. How did you forget the sun?'” But because we talk about these things and we openly mention these and it’s, you know, we all have a good laugh about it. Those moments for the next people of, “Oh, I discovered the galactic plane in my observations.” It’s just that moment of like, “Oh, that was my sun. I’ve had my moment. I discovered the sun.”
And inevitably it’s going to happen. Everyone will have that moment. It even happened to Nobel Prize-winning scientists who put out an international telegram to say they found a fancy new transient, something that’s popped up in the sky that wasn’t there. And it was Mars and then they had to come back being like, “Just kidding. Never mind. Don’t worry about that one actually.” So everyone! happens to do all of us.
Linda: And when your brain is constantly looking to support the hypothesis that I am stupid and I am the only one that could possibly have happened to, then other people telling those stories is fundamental.
Kat: And once again, your brain would sift through the data to only find the data that it needs to back up its hypothesis.
Linda: Yes. Confirmation bias is particularly problematic in the case of imposter syndrome. And that segues nicely into one of your passion projects. I shouldn’t describe it as a passion project. In fact, I pulled a friend up on that in reference to my work recently. One of your other projects that you don’t get paid for, probably, inspire her. Tell me about that.
Kat: Include her.
Linda: No, include her. Sorry.
Kat: That’s okay. I mean, I quite like inspire her also. It’s not inaccurate. That’s still very much. Very much related. So this was work I was doing. So at the end of my honours, I knew I wanted to do a PhD and I actually didn’t get into the PhD program that I wanted to. So I was kind of in the same boat.
Linda: Oh snap, neither did I!
Kat: Yeah. I was sort of like, oh my gosh, do I have to rethink my entire life? I’ve been working towards doing a PhD in astrophysics and then I didn’t get in. And it was just a big if for me, but I was approached by someone to work in their group on physics education research. So specifically looking at how we can teach teachers so that they can teach students better and using methods where students will not only understand the information better, but retain the information for longer also. And so that was part of my work. I was helping introduce new curricula in New South Wales for physics for high school students and trying to develop resources and workshops for teachers to help them with this incoming syllabus.
It was actually a male colleague at the time who pointed out that in this new curriculum, in the radio activity section, there’s no mention of Marie Curie who coined the term radioactive two Nobel prizes in radio activity. Discovered the entire field, you know, absolute pioneer, and she’s not mentioned. And I was like, that just that doesn’t quite make sense. So I decided to go through and see why is she not mentioned. Is it just like we’re not mentioning anyone or who is mentioned and how are they mentioned?
And it was basically where I’ve learned, you know, once you see it, you can’t really unsee it that this narrative of how we were teaching science was only through the lens of this lone male genius. that it was discovered by a single man. Who was inevitably a white man in Europe in the Enlightenment period. And that was just when science was done. That was when all of the science was done. And we’ve actually solved it all now. There’s nothing left. It was all done in that period. And so not a single woman was mentioned in the entire physics curriculum, but there were, you know, 20 odd men mentioned like 50 times.
So it was this stark reality of I’ve been dealing with this crippling self doubt and imposter syndrome because there’s no one like me. And I’m already well aware that anyone like me, I’ve kind of already pushed beyond where people like me generally tend to go. You know, I watched everyone dropping out of my undergraduate degree and it just sort of dwindled till I got to this point.
So now I’m seeing that this narrative is being reinforced. This is what we’re teaching our students that not just as it not for women. If you’re interested in physics, your career ends at high school and that’s it. And if you want to do physics, if you want to do science, you should be a man in Europe. That’s where it matters. And so it’s just a wildly incorrect narrative and also so damaging to all students, not just women, but all students really suffer from that.
So this was really the start of Include Her. And it is also a passion project because I am so passionate about it because it is so closely related to my own personal experience. And this is one of those things where I as a scientist cannot extract myself exclusively from this data and this work. I am fundamentally a part of where this research started and how we’ve chosen to do this research.
So Include Her is now an international team based in Australia, the UK and America. And we have done a full curriculum review of all science courses in Australia for year 11 and 12 students.
Linda: That’s amazing.
Kat: Environmental science, biochemistry and physics. And we looked at who was mentioned and how they were mentioned. Are we are only mentioning scientists that are, you know, it’s Einstein Laws where you’re not really mentioning Einstein the scientist, that’s something that’s named after him. Or are we saying, look at how Einstein revolutionised physics in which case you’re really looking at the scientists themselves. And we found across Australia, there are 145 scientists that are mentioned. Do you want to take a stab at how many are women?
Linda: I think it’s going to hurt.
Kat: It’s definitely going to hurt. It’s just the half-much will it hurt really?
Linda: 10?
Kat: Just one.
Linda: Oh my God.
Kat: Not even in double digits. No, just one. And she’s only mentioned in Queensland, Northern Territory and South Australia. So any of the other states and territories, too bad. Nothing for you. And it’s not even Marie Curie, which most people assume it would be her. It’s actually Rosalind Franklin. So if you look at the way that she’s mentioned, it’s often still in the narrative of a man, i.e. Watson and Crick stealing her work and winning the Nobel Prize for it.
So it’s actually less about her incredible methodology. She developed an entirely new method for imaging, no, none of that. It’s about Watson and Crick winning the Nobel Prize for her work. So include her is really, that’s where we started is looking at the curriculum, but we’ve grown a lot since then. It’s about changing the narrative that we all hold about what a scientist is, how science is done.
A lot of what we’re discussing today is related to that. It’s this perception that scientists are immune to the emotion that comes into their research, that we are fundamentally completely unbiased and not emotional in what we’re doing. And that’s not at all true. It’s never been true. So science will always have that level of bias, will always have that level of emotion because it’s done by scientists who are not free from those biases.
So how can we change the perception that science is done by a lone individual having a eureka moment and instead look at the fact that science is done by everyone around us and has always been done for well beyond the enlightenment period. We look at some of the oldest scientists and innovators here on this continent and it’s a different, there’s not necessarily one way to do science. Science is for everyone.
There’s different approaches, there’s different areas and we’re facing incredibly complex and terrifying issues across the world today. The way that we are approaching them with this stale idea of science is not going to tackle them. So we need to be teaching science in a way that allows for innovation, that allows for the people to come through with the perspectives that we’re fundamentally lacking to address these issues as well.
So we’re looking at changing curriculum but also working on workshops. We’re doing hopefully very soon a big national rollout of our workshop that we’ve been developing that really sits there and breaks down the narratives that we hold of various science and scientists, science discoveries and trying to shape a different view of what science is and who can do science as well. That was a very long rambling introduction to it.
Linda: Oh, no, I was riveted. That’s wonderful. And I can absolutely confirm the issue because not that it needs confirming, like it’s right in front of you, but also when I first wrote, I wrote a talk called Raising Heretics which then became the start of my book Raising Heretics. And I gave the talk in front of my friend, Nicky Ringland, and she was like, “That was fantastic. But also… all of your scientists, all of the heretics were men. Maybe we could fix that.” And I was like, “Oh my God, I didn’t even see it because it’s what we always see.”
Kat: It’s just what you do!
Linda: So you just don’t even see it. And so I actually went out and found a bunch of amazing women who were heretics and put them in the talk and it just made it so much stronger and so much more relatable.
Kat: And it’s much more interesting.
Linda: Yeah, but it took someone else to call it out. Like I didn’t say it. I’d written it. But if you sit there and say, “Name five scientists most people will go, men, men, men, men, men.”
Kat: Yeah, absolutely. And then if you ask for women, they’re like, “Oh, Marie Curie.” Maybe you’ll get Rosalind Franklin, but you’re unlikely to get many more than that. It’s been an interesting response that a lot of people have where I say we’re not learning of women in science today. The way that we’re teaching science, women are just absolutely excluded from this narrative. And people will come back saying, “Well, I don’t know of any women scientists. Therefore, they don’t exist.” And you’re like, “Oh, it’s just, it’s so close to the point. You don’t know of any women scientists. That is true.” Could that be because you never learnt of them? And just that jump of a different interpretation from the same data, the same result, is just too much. And it’s, you’re so close, so frustrating.
Linda: You nearly get it!
Kat: you nearly get it. You’re just so close. I have the same realisation. I mean, even in my early work of saying, for Includ Her, we should be including women. There are women that are easily includeable, includeable, if that’s a word, into the curriculum that we have. There’s easy spots where you’re not even changing the content and you can just put the names of women that have done this work.
Linda: We’re already talking about their work in a lot of cases.
Kat: Exactly. It’s usually just that it was sort of spontaneous knowledge that came out of nowhere and no one really did it. But if you put the names of women next to it, you can increase the representations like 30% without really trying.
But I was doing presentations of this and I actually had a colleague who I’m very thankful that they took me aside afterwards. And they were like, “I love this presentation. I thought it was really great.” But every example that you gave was actually just a white European woman of the women that you should be including. And I looked at it and I was like, “Oh my gosh, I’ve just done the exact same thing.” And so even though I was there advocating for introducing a change, for giving credit where credit is due, even I’m holding these biases because it’s still embedded, this perception that all science is done in Europe and that is the only science. And I laugh when we get taught things like the Dark Ages, that science just stopped. Everyone was dying in Europe and that was all that we could focus on in science just didn’t exist. But that’s not true. China was thriving. India was moving along like nothing else. It’s just this view that Europe is all that happened. That is the only history we need.
And so we get this perception that the world just went to a standstill for however long the Dark Ages were. And that’s just so incorrect. And so we’re missing all that innovation. We’re missing all that science. Those narratives that are also part of the reason why people are intrigued in science and want to study science as well.
So it’s not just about putting the names of women there, but it’s also about changing the narrative of how we think science is done. It’s not done by an individual. It’s groups together working slowly towards an interpretation that makes it.
Linda: And that storytelling is so important. Stories are how we engage, how we understand and relate to the world. And we’ve got to get better at telling stories.
Kat: Absolutely. Yeah. This has been so fun. What excites you about Data?
Kat: Oh my gosh. What excites me about Data? I think the puzzle of it, the absolute mystery. I love that it’s like you’re sitting there and you don’t quite know what you’re going to find. A lot of observations, you know, even if everything went to plan and I’ve processed things with the same pipeline that I’ve done every time. I mean, I’m processing at the moment a sky survey using data that’s very familiar, a pipeline that’s been, you know, working smoothly for many years now, and I helped develop and everything’s good, but still if I click go on processing this data, there’s always something different in it. There’s always something weird that requires looking at it.
And so I love just sort of piecing together that “what on earth is going on”. And the, the, because, you know, maybe it’s ADHD in me that if I was just doing the same thing over and over, I’m inevitably going to get bored and just processing data. You know, you say it like that. You’re like, oh, I processed data. I processed thousands and thousands of observations each week. And that just sounds so not interesting. But instead you look at it, I’ve created this image of galaxies and there’s like a weird splodge that stripes across the sky. And I’m like, there’s no satellites in these observations. So what’s happening? and trying to figure it out and then finding, you know, one random little feature that if the telescope is pointing low on the horizon over here on the complete opposite side of the horizon, if a bright source is there, you get a splodge in your image, even though you’re not looking there. That’s such a niche example that we hadn’t come across. And I, I love being able to find these things.
So I think my favorite part about data is that it is completely unpredictable. And that may also be my least favorite part about it. My favorite part also!
Linda: That makes perfect sense.
Kat: That’s also my favorite and least favorite thing about coding, my favorite thing is that coding does exactly what you tell it to do. And my least favorite thing is that coding does exactly what you tell it to do.
Linda: That’s so relatable. Thank you so much. This has been a fantastic conversation. And I love the way there’s like a thread running through. I ask everyone the same questions, but the conversation goes everywhere, but there are threads running through like the connection to the last episode is so strong. And then there was other stuff you said that was like Sarah Beacroft also kind of talked about. And it’s just, it’s great. It’s so good. Thank you so much.
Kat: Thank you so much. This is so fun. I always love talking about fun problems, weird things in my data. Great.
Linda: Fantastic.
