How to think like a physicist?

Daniel and Jorge discuss whether physicists think about the world differently, and how that makes them more or less useful.

See omnystudio.com/listener for privacy information.

Episode Transcript

Speaker 1 (00:07):
Andy Daniel, Did you always want to be a paid physicist?

Speaker 2 (00:11):
Definitely not. When I was a kid, I did not
want to be a physicist.

Speaker 1 (00:15):
Really, you knew what it was, but you knew you
didn't want to be one.

Speaker 2 (00:19):
I don't think I understood what a scientist was well enough.
But when I was a kid, I wanted to be
an explorer. I wanted to get on a ship and
find some new island and name it after myself.

Speaker 1 (00:29):
You just want to get out of Los Alamos.

Speaker 2 (00:32):
Main purpose here, Yeah, though you can't really take a
ship out of Los Alamos because it's landlocked. So there
were some basic problems in my thinking.

Speaker 1 (00:39):
Well, you could take a train and then a ship.
But don't they say everyone's a physicists, especially little kids.

Speaker 2 (00:45):
Yeah, I think everybody is a scientist because they're curious
about the world. And in the end I discovered that
being a physicist it's kind of like being an explorer,
except instead of discovering new continents, we're trying to discover
new frontiers of knowledge.

Speaker 1 (00:59):
Instead of surfing waves out there in the sea, you're
surfing the couch.

Speaker 2 (01:02):
Mostly, I'm clickly clacking my way to new shores of knowledge.

Speaker 1 (01:10):
Just don't get scurvy on your couch.

Speaker 2 (01:13):
I got a bowl of limes here next to me.

Speaker 1 (01:15):
Okay for with the tequila and the margaritas. That's for
after work, after work work these days? What's the difference. Hi,

(01:40):
I'm Hori. I'm a cartoonist and the author of Oliver's
Great Big Universe.

Speaker 2 (01:43):
Hi, I'm Daniel. I'm a particle physicist and a professor
at UC Irvine, and I want to teach people to
think like a physicist.

Speaker 1 (01:49):
Wait, I'm confused. If everyone's a physicist, aren't just teaching
people to think like humans?

Speaker 2 (01:56):
Yeah, basically, I'm done. I can retire. It's after work time, margarita.

Speaker 1 (02:01):
I know, let's get the shots going.

Speaker 2 (02:04):
No, I think everybody does have curiosity. But you know,
it took us a while to figure out some tips
and some tricks to effectively extract knowledge from the universe
rather than just like, you know, making up cute stories
to satisfy our curiosity.

Speaker 1 (02:17):
Right, it probably took a while to get paid to
do it too.

Speaker 2 (02:20):
Yeah, that's certainly true. A lot of the big names
in the history of science were men of leisure, you know,
operating on their trust funds or daddy's bank account.

Speaker 1 (02:30):
Who do you think was the first professional physicists?

Speaker 2 (02:33):
You know, science as a profession is not actually that old.
It's something like in the late eighteen hundreds that people
started to call themselves scientists and get paid to do it.
There are money to hire people to do this kind
of research. Until then, it was you know, natural philosophers
and people just sort of like curious, poking around in
their own laboratories. Yeah, but scientists as a job is

(02:54):
not much more than like one hundred years old.

Speaker 1 (02:55):
WHOA. So even the word science is relatively new.

Speaker 2 (02:58):
Yeah, exactly. If you ask like Gaos or Newton or
leading It or Aristotle, they certainly would not call themselves
a scientist. That's a new word.

Speaker 1 (03:07):
Or maybe they did it on purpose. They're like science,
No thanks, it's a new fangled thing that all the
kids are talking about. I prefer to be a natural philosopher.
But anyways, welcome to our podcast, Daniel and Jorge Explain
the Universe, a production of iHeartRadio.

Speaker 2 (03:23):
In which we do our best to demonstrate what it's
like to think like a physicist. We take a physicist
approach to dismantling the whole universe, understanding all of its
little bits, building mental mathematical models to try to explain it,
asking questions of those models, and then wondering what does
it all mean anyway?

Speaker 1 (03:40):
Yeah, because, as we talked about before, the universe belongs
to everyone, and asking questions is everyone's job, but a
few people get to do it as a career.

Speaker 2 (03:51):
Get to Yes, exactly. It's definitely a treat and a privilege.

Speaker 1 (03:56):
Well, you get paid to do it, I guess, and
to do that, there's a certain mindset you have to have,
right in order to be part of the profession.

Speaker 2 (04:03):
Yeah, there definitely is a way of thinking that's sort
of like a physicist way of thinking. And I see
this because people who are trained as physicists and then
go out into the world and work in other areas
chemistry or engineering or computer science still take with them
a certain mindset, a certain way of approaching problems, which
can be really really helpful and useful or also sometimes

(04:25):
frustrating for their colleagues.

Speaker 1 (04:27):
Yeah, no, I can totally relate. I think that also
the same is for engineers. You know, anyone who studied
engineering definitely thinks like an engineer is trying to think
it a certain way and a certain mindset about tackling
problems for sure.

Speaker 2 (04:39):
Yeah, absolutely take an engineering approach to cartooning.

Speaker 1 (04:43):
For example, Yeah, whenever I draw a bridge, I mean
I really put some calculations behind it, why to make
sure it doesn't fall down?

Speaker 2 (04:51):
Yeah, I know all those cartoons could be injured. I mean,
think about their families.

Speaker 1 (04:55):
Yeah. I usually build in a safety factor of like
two or three to every cartoon, just in case. But yeah,
but professional physicists do think about things in a very
different way than the rest of us. And so that's
the question we'll be exploring today. So to the on
the podcast, we'll be taggling how to think like a physicist?

Speaker 2 (05:19):
And I'm not sure if this should be like an
instruction manual or like.

Speaker 1 (05:22):
A warning, Oh why what can happen?

Speaker 2 (05:26):
You know, like watch out for these signs that you're
thinking like a physicist, or like, hey, would you like
to think like a physicist? Here's steps one, two, five.

Speaker 1 (05:34):
Well, I guess if it was the former, we should
title the eposite, hard to Not Think like a Physicist?
How to avoid thinking like a physicist.

Speaker 2 (05:41):
We're going to get into the positives, I'm sure, but
you know, there is this lore that sometimes physicists oversimplify things.
They're like, come into a new field, They're like, oh,
these can just approximate this with a sphere, maybe a
line on it or whatever. There's this urban legend that
physicists being too simplistic or the cause of the two
thousand and eight financial collapse, for example. So you know,
there are potentially some dangers to applying physics thinking to

(06:03):
the broader world.

Speaker 1 (06:04):
Daniel, I wonder if you're overestimating how much people think
about physicists.

Speaker 2 (06:08):
Probably I definitely don't have a clear view of that.

Speaker 1 (06:11):
I mean, I think for an urban legend to exist,
do you sort of need urban people talking.

Speaker 2 (06:15):
About Maybe that's just an urban legend within physics, maybe
nobody else.

Speaker 1 (06:22):
It's like a yeah, just have issues.

Speaker 2 (06:28):
We definitely do.

Speaker 1 (06:30):
But it's an interesting question to ask if you're thinking
about following a career in physics, or wondering what is
the job entail and what kind of mindset do you
have to have in order to do it at a
research university or to become one, or to get a
degree in.

Speaker 2 (06:44):
It, or if you're just an armchair physicist, if you
like thinking about the nature of the universe and making
progress and over the years, maybe while listening to this podcast,
you've been putting together your own personal mental model of
the universe, asking questions, trying to click it together, coming
to a holistic understanding of how things work. In that case,
you might have picked up a few of the tricks

(07:05):
of thinking like a physicist.

Speaker 1 (07:07):
Well, as usually, we were wondering how many people out
there had thought about this question, had maybe wondered what
it's like to be a professional physicist and what kind
of mental skills you need to be one.

Speaker 2 (07:16):
Thanks very much to everybody who answers these random questions.
Love hearing your thoughts. Please don't be shy if you
want to join the group, just write to me to
Questions at Danielandjorge dot com.

Speaker 1 (07:27):
So think about it for a second. What do you
think it takes to think like a physicist? Here's what
people have to say.

Speaker 3 (07:33):
A physicist must think of small and apply it to
the infinitely large universe, and that's not easy to do.
Hence the podcast for the rest of us.

Speaker 4 (07:47):
What if that's an expression? I haven't heard of it before,
so it's value. I think it probably would refer to
someone being very practical, someone following the scientific method very dogmatic, accurate.
But then some theoretical physicists that are a bit wacky
in what they come up with, so possibly a little
bit of that too.

Speaker 2 (08:06):
I think like a physicist is to be asking questions
and be relentless in your quest for an answer. I'd
say thinking like a physicist means being curious and searching
for answers through trial and error and experiments.

Speaker 5 (08:20):
This means there's our podcast about physicists, and to do
it with your cartoonist friend.

Speaker 6 (08:27):
Basically, to think like a physicist means if you discover something,
you get to really terrible name that doesn't make sense.

Speaker 5 (08:39):
I think it means to contemplate matter and energy and
their interactions with one another.

Speaker 7 (08:45):
Well, from the episodes that I have listened to so far,
I would that to think like a physicist means to
be inquisitive, to try to make connections between different aspects
facets of life, and wondering why and.

Speaker 2 (09:03):
Trying to.

Speaker 7 (09:05):
Better understand and explain the phenomena we see throughout our
daily lives.

Speaker 1 (09:10):
All right, I like some of these answers. I guess
we're done because one of them said, we just need
to start on a podcast about physics.

Speaker 2 (09:20):
And then give everything you discover a terrible name. These
are some juicy answers.

Speaker 1 (09:26):
I guess people have been listening to our podcast.

Speaker 2 (09:29):
I love these answers because there's so meta. They tell
me basically what people have learned from listening to the
podcast for all these years. It's fantastic.

Speaker 1 (09:37):
Well, hopefully people are thinking a little bit more like scientists,
like rational thinkers because of this podcast, and also maybe
learning a little bit more about the universe and how
it all works down to the atomic level and the
galactic level.

Speaker 2 (09:50):
Yeah, and not just absorbing facts and little bits of knowledge,
pieces of information, but also training yourself into how to
accumulate more information, how to fit those pieces of information together,
how to think about them. Science is more than just
what we've learned. It's how we're going to learn more.

Speaker 1 (10:07):
How are we driving the distinction here between physicis and
just a regular scientist or do you mean how to
think like a scientist?

Speaker 2 (10:13):
Yeah, it's a great question. I don't know the answer
to that. I'm probably not even the right person to
answer the question of how do physicists think because I'm
stuck in that mindset. I can't really see outside of
it to understand how other people think. But when I
mean chemists or biologists or economists, I do notice that
answer and ask questions in a different way. There's something
I have more in common with other physicists than I

(10:36):
have with other scientists. So there's something to it.

Speaker 1 (10:39):
All, right, Well, let's dig into it. What do you
think is specific about how physicists think.

Speaker 2 (10:44):
I think some of it comes from the fundamental motivation
and the assumptions that underlie physics. Like the goal is big.
We want to understand the universe. We want to figure
it out. And the assumptions are pretty basic. They're like, look,
the universe is understand a bull, and we can't describe
it with mathematical laws. We can build a mental model.
The model should follow those laws, and we can use

(11:06):
it to like predict the future and to understand the
nature of the universe. You know, inherent in that is
that we are simplifying the universe. We're taking all these
observations and the weaving them together into a story. That's
what the mathematical model is. We're saying, here's how this works,
here's what's really happening behind the scene. So there's sort
of like an ambition there to say, like we can
describe the basic elements of the universe, whereas, and again

(11:29):
I'm not an expert in other fields, you know, they
feel a little bit more zoomed out, so they're not
always as ambitious about like the fundamental understanding. They're describing
things as sort of a higher level, which again still
requires mathematical modeling and great precision. It's not a question
of like precision or rigors, just a question of like
the ambition the context of the questions you're asking.

Speaker 1 (11:50):
Well, well, are you saying that other scientists are not
as ambitious?

Speaker 2 (11:54):
I think maybe philosophically, physics and at least fundamental physics
and particle physics is asking more ambitious questions than other fields. Yeah,
I think they have deeper and broader implications again philosophically.

Speaker 1 (12:08):
Right, right, So you think your topic of research is
more important than other scientists because you're a physicist. I'm
just saying there might be a little bit of a
bias here at Daniel.

Speaker 2 (12:18):
No, it's totally reasonable to dig into that. I wouldn't
say more important. You know, somebody who's developing new techniques
to develop green energy, for example, they're not answering deep
and fundamental questions about the nature of reality. But they're
improving people's lives and maybe saving the planet, so that's
arguably much more important. But I think in terms of
the philosophical context of our lives, particle physics and fundamental

(12:42):
physics is answering those questions. Whether that's important or not
is totally subjective, you know, whether it has value. Every
kind of science is answering different kinds of questions, giving
different kinds of insight into how the universe works. For me,
at least one of the appeals of fundamental physics are
these philosophical implications of it.

Speaker 1 (13:00):
Right, Well, I think, you know, most scientists would agree
that what they're doing is also trying to understand and
explain the world. I wonder if maybe a lot of
the difference is just in the topic and the kinds
of things that you're looking at the scope of it,
or the kinds of phenomena you're looking at.

Speaker 2 (13:16):
Yeah, I think that everybody is doing the thing they
think is most interesting and most exciting, and that's very personal.
Right the person who's like crouching in a rainforest watching
spiders crawl up twigs for hours and hours a day,
is deeply fascinated by that and chose to do that
instead of economics or psychiatry or whatever for a reason.
And that's totally cool. So you're right, and the choice

(13:38):
of topic is very, very personal. But I think the
choice of topic also sometimes leads to a different way
of thinking. Like I think, because we're trying to ask
fundamental questions and deep questions about the universe, we feel
like we can touch onto some sort of mathematical purity,
that there is maybe mathematics that describes this that we
can drill down into and reveal, you know, somebody who's

(13:59):
studying like hurricanes. You know, we don't have any mathematics
that describes hurricanes. So we can do some simulations, but
we're sort of at a loss because of all the
chaos and the details. But when you zoom down into
the fundamental firmament of the universe, we hope maybe there
is some mathematics there that can describe what's going on.
And so that's I think why physicists tend to build
these mental mathematical models, sometimes too simplified, you know, hence

(14:22):
the famous spherical cowjoke, which I don't know, maybe that's
only a famous joke within physics.

Speaker 1 (14:26):
You tell me, I've never heard of that before. But
you know, I think all scientists would say that what
they're doing is fundamental as well. Like, if you're studying spiders,
you're probably thinking about the different ways that life can form,
or the different factors that go into creating life and
the factors that shape live That seems pretty fundamental as well. Yeah,
and ambitious.

Speaker 2 (14:46):
M h. And if anything, I think you probably have
a lot more insight into this than I do, or
than most people, because you interact with so many different
kinds of scientists and obviously you've been spending a lot
of time learning about physics and decoding the brains of
physicists but also other scientists, And so from your perspective,
I'd be very curious to hear, like, do you think

(15:06):
physicists think differently? Is the mind of physicists trained at
different skills? Do they take a different approach or all
scientists just one category?

Speaker 1 (15:13):
For you? You know, I think that if you're a scientist,
you're probably trying to figure out how the world and
the universe works. You're just asking questions about different phenomena
in it. You know, if you're someone who studies hurricanes.
You're trying to understand how certain physical processes work and

(15:33):
how they can come together to create large effects. For example,
that seems pretty fundamental as well or as fundamental as
asking you know what an atom is made of of?

Speaker 2 (15:43):
Yeah, can spiders come together to make hurricanes? Wouldn't that
be awesome? And shouldn't we pitch that show to the
Discovery Channel?

Speaker 1 (15:49):
Spider NATO's.

Speaker 2 (15:51):
Spider Cane.

Speaker 1 (15:54):
So I don't know, Sorry, science Spider NATO's sounds like
a winner.

Speaker 2 (15:59):
Yeah, well, I can't tell you whether it's fundamentally different
from the way other scientists think, because I'm not other scientists.
Maybe you can comment, but I can try to keep
you a little bit of an insight into the way
I approach a problem with the way I think about problems,
and that's this reliance on building a model. You know.
I look at a science problem like where is that
ball going to land after it comes off the bat?

(16:19):
Try to predict that? And I think, well, to get
that exactly right is way too complicated, And there's so
many factors involved. There's the wind speed, there's that bird
flying by, there's tufts in the air, et cetera, and
so I build a simpler model of the universe. I say,
toss out the real universe. Can we come up with
a simpler version of the universe and ask the question
in that universe, but build a model in such a

(16:40):
way that the answer in the simpler universe is still
relevant to reality. So can we extract the crucial details
of the problem, put those into our model, and then
use that to answer the question. So you know, you
don't care, for example, about the color of the ball,
you don't care whether some kid in the stand is
eating ice cream. None of these details about glorious reality
matter to answering this question. So you build the simpler

(17:00):
model specific to that question because it's good at answering
that question, not every other question. And you use that
to answer the question. And you know, you can argue
philosophically like is that model real, what does it mean
about the universe if it works, et cetera, et cetera.
But that's sort of to me, the core of thinking
like a physicist is building a little mental model and
then using that to answer your questions.

Speaker 1 (17:20):
Yeah, I think you're basically describing what any scientist does.
You know, chemists, biologists, they all work off models. I
mean probably the word model is the most used word
in all of science. Yeah, you know, biologists make models
about evolution, about gene interactions, about how molecules interact, or
how a species propagated, and things like that. But I

(17:42):
wonder if the difference with you is that you're making
models about the physical world or about baseballs, for example,
and not spiders.

Speaker 2 (17:51):
Spiders are just way too complicated. There's no way for
me to build a model of a spider. I have
no idea exactly, and I know how to make the
approximation so that I can describe a baseball. I know
what to ignore. Maybe that's just my physics intuition, but
I don't know how to do that for a spider,
and I want to push back a little bit. I
do think there's a difference between the models built by

(18:13):
physicists and those built biologists, for example. I mean, in biology,
we know that every model we build is effective. It's
not fundamental. It's describing some emerging phenomenon like butterflies or spiders.
Something we know is not an inherent object in the universe,
but made out of those bits. It comes together through
a special arrangement. So biology isn't describing something inherent to

(18:37):
the universe. It's just approximately describing how things work during
special conditions where like spiders and butterflies happen to emerge
because they don't always right. There's a long time in
the universe without spiders and butterflies, and so those rules
don't apply in those scenarios. But physics is trying to
figure out the fundamental laws, those that always apply in

(18:58):
all circumstances that are inherent to the universe. And that
difference in goal, I think leads to a different way
of thinking, you know, good or bad. It leads to
a hubrist that we can describe anything with simple laws,
and it leads to different approaches and in different scientific culture,
so that physicists are kind of recognizable to others and

(19:19):
also to each other.

Speaker 1 (19:21):
Well, you're married to a biologist, how does your way
of thinking different from your spouses?

Speaker 2 (19:27):
Yeah, I think something that's different in between the way
that I think about things and the way biologists like
my wife think about things is we're definitely much more
focused on questions of like uncertainty and making things quantitative
in order to try to extract some knowledge. Sometimes the
things we're dealing with are abstract or indirect. You know,
we're talking about tiny particles or things we can't ever

(19:47):
see or even struggle to visualize. And so to help
us guide our thinking, we rely really heavily on the uncertainty.
How well do we know this? What can we say
about this? Because we don't have much intuition, we can't
like always got check our answers and say, is that
reasonable that the top quark lives for ten to the
minus twenty three seconds? I mean, you can't see that anyway.

(20:08):
Whereas you know, my wife she can look at stuff
and oh is it growing? And did we get this right?
Is this virus killing that bacteria? Is somebody's got health
improving when they eat more chia seeds, this kind of stuff.

Speaker 1 (20:19):
But she works with models as well, right, Her.

Speaker 2 (20:21):
Grad students are really good looking. Yes, they're like models.

Speaker 1 (20:25):
Yeah, yeah, well in comparison to physicists, I'm sure.

Speaker 2 (20:28):
Ooh, you're right though, that models is a very abused word,
Like I also work in the machine learning community, and
their model means to make very very different than a
model in physics, than a model in fashion, and so
it's a very generic word unfortunately.

Speaker 1 (20:43):
But you think that maybe it's something to do with
the way that you look at the world and you
formulate models. But I guess I'm trying to say that
I think that's what all scientists do, right, across different fields.

Speaker 2 (20:52):
Yeah, so maybe physicists have more in common with other
scientists than I ever imagined. Happy to.

Speaker 1 (20:58):
Sounds like you need to talk to people outside your
department a little more, baby, besides your spouse, horten to.
You interact with the economists or chemists.

Speaker 2 (21:08):
Economists very rarely, only if I run into them at
the park. Chemists and computer scientists and engineers much more common.
We sometimes have problems in common, you know, working on
electronics for a new technology we want to bury in
the ice in Antarctica, we need to understand the engineering
details of it, or thinking about how to apply machine

(21:28):
learning techniques we've developed for neutron stars to the problem
of like predicting organic synthesis, these kind of things. So, yeah,
definitely interact with the more physical science and engineering people
more often than like psychiatrists. But I also talk to
philosophers quite a bit. I don't know if they qualify
as scientists.

Speaker 1 (21:44):
Do they I think they're not usually They're not in
the same department for a reason, isn't it.

Speaker 2 (21:49):
It's fascinating though, Actually people in the philosophy of physics
department here, they all have their PhDs in physics rather
than in philosophy.

Speaker 1 (21:57):
Well, so they're physicists who have alosophy degree in the
philosophy of science physics.

Speaker 2 (22:05):
That a doctor of philosophy of physicists, but now they're
professors in philosophy of physics.

Speaker 1 (22:12):
It sounds like what is it the snake finally ate
its tale. It is interesting to think about how people
who are paid to do physics in particular think and
what kinds of what makes them a tick I guess,
and how does that color how they see the world,
and so to get more insight into that, Danielle, you
interviewed a couple of physicists and one ex physicists.

Speaker 2 (22:35):
Yeah, that's right. I talked to one physicist who's made
it her mission to explain to people how physicists think
about uncertainty, and another whose job is to guide physicists
into the real world to find positions outside of academic
physics and research.

Speaker 1 (22:51):
Well, it sounds like these are sort of like physics
translators or physics counselors.

Speaker 2 (22:59):
Yeah, exactly, trying to bridge the gap between physicists and
actual human beings.

Speaker 1 (23:04):
All right, well, when we come back, we'll listen to
Daniel talking to two physicists whose jobs it is to
translate what physicists think and do to the rest of
the universe. So we'll dig into that, but first let's
take a quick break.

Speaker 8 (23:31):
Bar.

Speaker 1 (23:31):
We're asking the question how to think like a physicist
and apparently that involves talking to more.

Speaker 2 (23:37):
Physicists group think like a physicist?

Speaker 1 (23:44):
All right, well, you got to interview you too interesting
people here, Daniel. First one is doctor Jen Kyle. What
does Jen Kyle do?

Speaker 2 (23:51):
Jen Kyle is a theoretical physicist, but she also runs
the YouTube channel Think like a Physicist, where she tries
to explain to you how do you use the techniques
and tricks of physics to think about the world and
also to decode science results so you can get an
understanding for whether that newsflash you just read about black
holes is real or not?

Speaker 1 (24:12):
And did she talk to non physicists to figure out
if a physicists thinking of unique way.

Speaker 2 (24:18):
Through her YouTube channel? So yeah, via the comments section.

Speaker 1 (24:21):
Oh boy, and we all know how productive. Those can be.

Speaker 2 (24:26):
Great insights in the comment section as always.

Speaker 1 (24:29):
All right, well, here's Daniels interview with particle physicists and
YouTuber Jen Kyle.

Speaker 2 (24:38):
So it's my pleasure to introduce the podcast doctor Jen Kyle. Jen,
thanks very much for joining us today.

Speaker 9 (24:44):
Hi, great to be here.

Speaker 2 (24:46):
Great. Tell us a little bit about yourself. What's your
background with your training? What are you up to now?

Speaker 9 (24:52):
Ah?

Speaker 5 (24:52):
Well, I'm a theoretical particle physicist. I've done mostly work
beyond the standard model physics. I've looked at some things
on dark matter and possible new theories of flavor in
the Cork and Lepton sectors, and I basically dabbled in

(25:14):
physics beyond what we know now.

Speaker 2 (25:16):
Great, so you are definitely a trained and practicing physicist.
So tell me what does it mean to you to
think like a physicist? Can you remember learning how to
do that? Can you compare the way you think now
to the way you thought before you went to grad school?
What does it mean to think like a physicist?

Speaker 5 (25:34):
I would definitely say it was not something that one
learns in one day. It's more of a practice that
you learn over many years. And I would say that
a large part of thinking like a physicist is knowing
how to draw conclusions from the universe and observations that

(25:59):
we make of it, but also always keeping in mind
how uncertain those conclusions that we draw from our observations
can possibly be.

Speaker 2 (26:09):
What do you mean uncertain, Like we have a hunch
and we're not sure. H we don't have enough information,
or we could be confused. What do you mean by.

Speaker 5 (26:17):
Uncertain Well, basically, we draw conclusions about the universe from
making observations and making measurements. So let's say that we
have some amazing new idea that someone has come up with,
but it hasn't been tested. It will make predictions about
the universe, and oftentimes these are predictions about the values

(26:40):
of certain quantities that we can measure, like the lifetime
of a particle or the rate of a certain process
that happens at the large Hadron collider. And we want
to test this new amazing hypothesis, so we go and
measure those quantities.

Speaker 9 (26:57):
And when we measure those.

Speaker 5 (26:59):
Quantities, we use experimental apparatuses and techniques, but it's not
possible to ever have a perfect experiment. Whenever you get
a measured value of a quantity, it's always going to
differ at least a little bit from the true value
of the quantity that you're trying to measure. So if
you try to measure the electron mass, you will get

(27:20):
a measured value of the electron mass, but it's not
going to be exactly the true value of the electron mass.

Speaker 2 (27:25):
So let's make a little bit more concrete instead of
thinking about particle physics. Let's say somebody gives me a
coin and I have a theory that this coin is
not fair, that it's going to favor heads right sixty
six percent or something, and then I can do an
experiment to see, well, is it a fair coin by
flipping it right five hundred times. So I think you're
saying that there's uncertainty because even if I flip it

(27:47):
a thousand times, I'm never going to know precisely what
the real probability is because I'm not flipping an infinite
number of times. There's always some randomness. Is that what
you're saying exactly? Okay, So there's uncertainty in our measurements
because we don't take infinitely long experiments and we don't
have infinite amounts of data. What are some other ways

(28:08):
that we can be wrong or uncertain about our conclusions.

Speaker 5 (28:12):
Well, there are lots of ways that error can sneak
into measurements. For example, we make measurements using some kind
of experimental measurement apparatus.

Speaker 9 (28:25):
So, for example, let's say if.

Speaker 5 (28:27):
We're trying to measure any quantity, we're using some kind
of experimental apparatus to do it, and that apparatus is
going to have a finite resolution of some kind. So
for example, let's say you're trying to measure the size of.

Speaker 9 (28:43):
An object in a room. You use a ruler, and
that ruler.

Speaker 5 (28:47):
Has a finite gradation on it. You can't see down
to the micron size using a ruler, so there's automatically
some level of uncertainty that's going to come in because
of effects like that. You may also for very complicated measurements, like,
for example, if you're trying to measure a cross section

(29:08):
at the large hadron collider, you have very complicated measuring
devices and you have to simulate various parts of the
not only the physics that you're trying to understand, but
the device, and those simulations will never match up exactly
well with reality.

Speaker 2 (29:27):
So I think what you're saying is that sometimes to
do these experiments, we have to use devices we don't
even really understand exactly how they work. Like if I'm
measuring an electron the lartadron collider, and I have some
device to measure an electrons energy, it's complicated to measure
an electrons energy, and I don't exactly know what happens
when an electron slams into a block of copper and

(29:47):
creates a huge shower of other particles. It's complicated physics,
and I could be wrong about what's going on in
my own experimental device that I built and designed. Right.

Speaker 5 (29:57):
Yes, in fact, we don't entirely understan stand our own
measuring devices perfectly, so we have to model them and
simulate them, and sometimes compare those simulations to data in
order to current to improve those simulations and get a
better measurement of whatever it is we're trying to measure.

Speaker 2 (30:15):
Right, So, like back to the coin example. You know
it's easy to look at a coin and say, oh
it's heads or oh it's tails, But say it was harder, right,
Say I couldn't just look at the coin. I needed
to have some little device that told me if it
was heads or tails, and that device I didn't really
know how it worked, and I wasn't always sure it
was correct. That would lead some like uncertainty into my measurement, right,

(30:35):
because it could be wrong, or I could think that
it's correct, but it's it's incorrect in some other ways.

Speaker 5 (30:41):
Yes, and it might be using some pattern recognition software
that doesn't handle like certain light levels very well or
something like that.

Speaker 9 (30:49):
So, yeah, it could make a mistake every.

Speaker 5 (30:50):
Once in a while until you you've got heads, when
you've got tails or vice versa.

Speaker 2 (30:54):
Yeah, And so in physics, we're very quantitative about this, right,
We're very specific when we measure something. We say, oh,
is a two percent chance we've been wrong, or a
zero point zer zer zer one percent chance we're wrong.
Why are we such sticklers about this in physics? Why
are we such nerds about measuring precisely how wrong we
might be in physics? What do you think?

Speaker 9 (31:14):
Well?

Speaker 5 (31:15):
I think that part of it is that physics was
one of the first fields to do a lot of measurements.
So if you're only doing ten measurements and you think
you'll screw up like one out of a thousand, you're
probably not too worried that you're that you're going to

(31:36):
produce a wrong result, or produce a result that had
a large statistical fluctuation where you didn't you didn't screw
up anything, and you're you're apparatus performed exactly correctly. But
nonetheless you've got very unlucky if you think that that
probability is small and you're only making like ten measurements,
you're not too worried that you're going to publish a
result that's going to lead people down a wrong path.

(31:58):
But in particle physics, we make thousands of measurements, most
of which you never hear about in the news because
unfortunately most of them agree.

Speaker 9 (32:07):
With the standard model.

Speaker 5 (32:09):
But because we make so many, there's going to be
some just out of statistical fluctuations that happen to appear
to disagree a lot with what we expect, and so
it's very important to have a very strict criterion for
deciding when something disagrees with what we expect so much

(32:31):
that it must be interesting.

Speaker 2 (32:34):
Yeah, I think that's probably true. Do you think it's
also because some of the things we're probing are sort
of invisible, so that our measurements are always going to
be indirect, you know, like if somebody discovers a new
kind of turtle in biology, they're like, here's the turtle.
Like I can show you, look, this is a turtle.
Like nobody's confused about whether it's a turtle. But if
we're saying, hey, I discovered the squigglyon, it's not like

(32:55):
I can say, I've got a pile of squiglyons. Here
they are. That's all play with them to show you data,
and the data has statistics, and we have to make inference,
and so it's always frustratingly indirect. And I wonder if
that's one reason why we have to be such nerds
about whether or not we've been confused, because there's so
many different steps between the physical reality and the actual
measurements we make.

Speaker 5 (33:16):
Yeah, it's also the case that in particle physics we're
also dealing with looking for processes in colliders that can
look a lot like other processes that we aren't actually
interested in.

Speaker 9 (33:29):
So it's not so much like we.

Speaker 5 (33:31):
Go out into the world and we find a new
turtle and we bring it back and show people and
say this is a new turtle. It's more like we
go out into the world, and we find a new
turtle that looks very, very similar to a lot of
other turtles, and we bring that turtle and another thirty
turtles back, and we show the collection of turtles to

(33:52):
our colleagues, and we have to convince them that that
one turtle really is special.

Speaker 2 (33:58):
It's not just the same turtle all the way down. Yeah, exactly.
And then we do experiments with those turtles, flipping them
to see if they're fair coins in that. So this
is the way that physicists think about things. We're really
focused on what we've measured, how well we know it,
quantifying that uncertainty different ways we can be wrong when
we communicate our results to the public. This is a challenge,

(34:20):
right to express to them here's what we think, but
here's how much wrong we might be. What do you
think are the usual stumbling blocks for people who haven't
spent their lives learning to think like a physicist for
understanding uncertainties and what we mean by uncertainties when we
talk about them.

Speaker 5 (34:38):
Well, I think one problem is that most of the
time in real life, when we're talking about needing to
know the value of some quantity, we.

Speaker 2 (34:48):
Were not hold on, are you contrasting physics with real
life is that what you just did here, are you
saying physics is not real for me?

Speaker 9 (34:56):
The same thing?

Speaker 5 (35:01):
But in the ordinary life, where we go outside and
you know, do things where we're not looking at a
computer screen, we do get values for various quantities. Like
if we're driving our car, we do look at our
speedometer hopefully and see what speed we're getting. And generally

(35:23):
the outside world isn't very it's not used to giving
us uncertainties on the numbers that we get. So we
look at that speedometer and it tells us we're going
fifty seven miles an hour, but it doesn't put an
error bar on it. And also when we're learning things
about either physics or anything else.

Speaker 9 (35:44):
In our in our education, at least in.

Speaker 5 (35:48):
Our earlier education, usually the idea is, here are the
principles that we work from.

Speaker 9 (35:53):
What can we figure out from it?

Speaker 5 (35:55):
But we don't actually stop and think, well, what are
the experimental results that led to us having those principles,
and what were the errors on those principles, what were
the uncertainties on those principles? And you know, how well
does that principle work with the situation I'm trying to
trying to study at the moment. Am I actually using
the right the right set of scientific principles for the

(36:20):
situation at hand, or am I introducing some uncertainties that maybe.

Speaker 9 (36:25):
Maybe I need to think about.

Speaker 5 (36:28):
So I would say that the main stumbling block is
that we just aren't exposed to it. It's it's hard
to come by.

Speaker 2 (36:39):
Yeah, I see. So maybe when you get pulled over,
you can tell the officer like, look, it said it
was I was doing sixty. I don't know why your
machine says I was doing eighty five. Maybe there's some
mistakes somewhere, right, Sometimes we have a little bit of
intuitive grasp of like maybe there's fuzz in the numbers.
But you're right, we're rarely like measuring the uncertainties in
quote unquote real life. So for people who are not

(37:01):
trained like a physicist and don't nerd out about statistics
all the time, well, it's a sort of intuitive or
easy way to start to think about these uncertainties. What
do you recommend? I know you have a wonderful YouTube
channel where you teach people to think like a physicist
and think about uncertainties. How should people get started thinking
about uncertainties like a physicist.

Speaker 5 (37:20):
Well, if you want to think about it the way
physicists do, I guess I would explain how physicists arrive
at those uncertainties. So, like a physicist who is conducting
some kind of an experiment, they are going to want
to produce a result, and they're going to want to
produce an error bar that goes with that result.

Speaker 9 (37:37):
That tells you and what the uncertainty on that result is.

Speaker 2 (37:40):
Let's stop there, firm and describe exactly what you mean.
They're like the error bar. So if I say I've
measured my speed to be seventy miles an hour with
an air bar of five, what does that mean? What
does the err bar mean? What am I saying when
I say five?

Speaker 5 (37:54):
So the error bar, if you're at least thinking about
it from a physicist point of view, is you've thought
about what the possible sources of error that can come
in the ways that you could be wrong, the ways
that you could measure it incorrectly, and you've done some
kind of analysis or thinking about it to add those

(38:16):
sources together and figure out roughly typically how much you
would be wrong by.

Speaker 2 (38:23):
So does that mean that if I measure my speed
to be seventy plus or minus five, that the true
speed is definitely within sixty five to seventy five, Like,
does the error bar completely define the possible extent of
the truth.

Speaker 5 (38:37):
Absolutely not. It's a typical value. It's a typical value
for the difference between the true value of something and
the value that we measure. And we don't know whether
the value we measure is above the true value or
below it. And we don't know if the difference between
the true value and our measured value is larger than

(38:59):
that error bar or smaller.

Speaker 9 (39:00):
Than that error bar.

Speaker 5 (39:01):
And an instance of a specific measurement, what that error
bar means is that's a typical value for how the
true value in the measured value would disagree.

Speaker 2 (39:12):
Right, And so if we quote seventy plus or minus five,
or let's talk about you know, politics, Joe Biden's pulling
numbers are forty four percent with a uncertainty of three percent. Right,
that doesn't mean that his true value is between you know,
forty four plus three and forty four minus three. It

(39:32):
means that there's a sixty percent chance that it is.
And then therefore there's a thirty two percent chance that
it isn't right. So the airbar tells us, as you say,
roughly the size of the expected difference between the truths
and the measured value. But it doesn't bound it right.
It doesn't tell us it's exactly within that. I see
this sort of misunderstanding all the time in political journalism.

(39:55):
You know, where they have two candidates and if they're
separated by ten points and the uncertainty is four points,
then they say, Okay, it's definitely a lead, but you know,
it still could be the opposite, or two candidates who
are near each other but within the statistical uncertainty, they
call it a tie, even though if one of them
has a larger value, we're pretty sure that you know,

(40:15):
we're somewhat sure at least that they have more support.
I think there's a lot of misunderstanding about what this
error bar means. It seems so much more definitive right
than the way that we meet it. It's really, as
you say, just a typical value. It tells you roughly
the scale of how far off you might be. So
when people are out there reading a scientific result, right

(40:36):
when they're not measuring their speedometer, when they're reading a
paper about a new particle and they come across something,
what should they be asking themselves? They should what should
they be looking for in that article to help understand
how uncertain are physicists about this new squeakly unparticle.

Speaker 5 (40:52):
Well, I mean, at the most basic level, if the
result is measuring something and saying this value was large
than what we were expecting from our prediction if the
particle didn't exist, the first question is to ask, well,
what was the difference between what was observed and what
was expected if the particle didn't exist, And then how

(41:12):
does that difference compare to the quoted uncertainty.

Speaker 9 (41:16):
So if that difference is a lot larger.

Speaker 5 (41:20):
Than the quoted uncertainty, then we would tend to think
that something interesting is going on. You know, maybe it's
particle discovery. Hopefully it's particle discovery, but it always could
be that something has gone wrong with the experiment that
we don't understand. On the other hand, if the difference
between what's observed and what's expected from the no new

(41:40):
particle hypothesis, if that difference is not much larger than
the uncertainty, or maybe it's only a couple times the uncertainty,
then it's probably a little bit too early to get excited.
We need more data and we need more results and
possibly more experiments to look at it before we say
anything definitive.

Speaker 2 (41:59):
Right, then make a concrete and go back to our
coin that we're tossing, or the turtle that we're flipping.
Let's say I flip the coin two times and I
get two heads, so it's one hundred percent heads, right,
And then I go off and I write a paper saying, look,
my coin is one hundred percent heads. It's totally unfair.
And you're the reviewer. You might look and say, all right,

(42:20):
you know, but the prediction for a fair coin is
fifty percent, and the prediction for an unfair coin is
you know, something above that. But the uncertainty on your
measurement is huge because you only flipped it twice, right,
So yes, you measured one hundred percent heads, but you
could have also gotten fifty percent heads or seventy five
percent heads or whatever. And so you're saying if I
go back and then flip it a million times and

(42:41):
I still get a million heads, that that's very different, right,
And I think people can understand that that's much more compelling.
If you get a million heads in a row, it's
very unlikely to be a fair coin. And that's the difference,
right that there's a smaller uncertainty on my measurement of
one hundred percent heads if I flip it a million
times and if I flip it two times times, And

(43:01):
so the two different hypotheses of like a fair coin
fifty percent heads and an unfair coin and one hundred
percent heads. The difference there is now large compared to
the uncertainty, whereas it was small when I only flipped
it twice.

Speaker 5 (43:13):
Yeah, when you only flip it twice, I mean, even
if the coin is fair, the probability is twenty five
percent it's going to come up heads both times. So
it's it's important to not jump the gun and think
that you've discovered something amazing when you might.

Speaker 9 (43:26):
Just have a quarter exactly.

Speaker 5 (43:30):
On the other hand, if you flip the coin ten
times and it comes up heads each time, well then
you know you start to think maybe something's up. And
if you do it twenty times, then you might start
to really think that's something up. And certainly, if you
flip it a million times, then you're pretty darn certain
something's else.

Speaker 2 (43:49):
Exactly, but I think it's fascinating that even now, for example,
we can't say one hundred percent definitively that the Higgs
boson exists. Like we've taken so much data, we have
so much evidence, and yet still it could all be
a fluctuation, right, It could all just be We could
be that situation where we flipped a fair coin a

(44:11):
million times and gotten a million heads in a row.
It can happen, and we could have been fooled by
our data. We don't have like a pile of Higgs
bosons we can point to and say these are them, folks.
We just have, you know, basically the result of flipping
a bunch of coins and seeing it come out weird
compared to our prediction for no Higgs boson. So in
principle we know we don't really know that any particle

(44:33):
is out there, though I guess as we continue to
make collisions and analyze data we get more and more certain.
But it's sort of like approaching the speed of light, right,
you can never actually get there.

Speaker 9 (44:42):
That's right.

Speaker 5 (44:43):
You can never be absolutely certain of any scientific result
that you produce. But on the other hand, you can
also not be absolutely certain that this chair sitting next
to you actually exists, because of course your eyes could
have malfunctioned, you could be dreaming.

Speaker 9 (44:59):
So one percent certainty is a dream. It's an illusion.
It's not something we can ever achieve.

Speaker 2 (45:06):
Exactly right, I'm not one hundred percent shortain we're having
this conversation. Yeah, exactly, Great, Well, so tell us more
about your project Think like a Physicist, where people can
go to learn more about it and learn more about
thinking like a physicist.

Speaker 9 (45:20):
Yeah.

Speaker 5 (45:20):
So I have a YouTube channel. It's called Think like
a Physicist. And the idea behind my channel is I
wanted to take the statistical methods, especially also the other
methods the physicists use, but especially the statistical methods the
physicists use, and I wanted to explain them in a
way that I hope non scientists can understand. And the

(45:41):
idea is that I would like for people when they
read about a scientific result and it has an error
bar on it, that they would be able to have
a better understanding of what that error bar means, and
also that that way they can understand scientific results in context.
For example, if you hear that one experiment does a

(46:03):
measurement of a certain quantity and it agrees with the
standard model. And then three years later you hear that
another experiment measured the same quantity and they got a
different result. You know, it might be because the second
experiment had a smaller error bar than the first one did,
and so you can understand results in context better.

Speaker 9 (46:25):
So basically, I go through a.

Speaker 5 (46:27):
Lot of the basic statistical techniques that physicists use, and
I hope that I explained them in a way that
people can understand. And so, yeah, I would very much
like the public to know more about these topics so
that they can understand what we do a bit better.

Speaker 2 (46:45):
Great, tell us one more time where people can find you.

Speaker 5 (46:47):
Yeah, my YouTube channel is called think like a Physicist.

Speaker 2 (46:50):
Great, well, thanks very much Jen for coming on podcast
today and thinking like a Physicist with me. I appreciate it.

Speaker 9 (46:55):
Thank you so much. It's been great.

Speaker 1 (46:57):
All right, interesting interview. I like the she talked about
uncertainties and how you know this concept, you know, spills
into our everyday lives, especially when it comes to things
like policies. But people don't seem to have a pretty
good understanding of that. Maybe they should talk to statisticians,
not physicists or politicians.

Speaker 2 (47:16):
How to think like a statistician exactly.

Speaker 1 (47:18):
Yeah, how to probably think like a statistician?

Speaker 2 (47:23):
How statisticians likely think?

Speaker 1 (47:26):
Yeah, likely think or think likely?

Speaker 2 (47:31):
The likelihood of me finding a good joke is low.

Speaker 1 (47:35):
Yeah, we'll make that the null hypothesis. All right, and
an interesting perspective though, about how to think like a physicist. Now,
let's talk to someone whose job it is to, I guess,
reintroduce physicists out into the world, sort of like those
wildlife experts who have to retrain animals to live in
the wild. Is that is that kind of her job?

Speaker 2 (47:57):
Yeah, exactly, Or re educate prisoners who are emerging.

Speaker 1 (48:00):
Oh, oh my goodness, I guess I could eat me
a sort of like a prison. There are walls, towers,
you know, small rooms where people are sitting all day.

Speaker 2 (48:12):
The food is terrible.

Speaker 1 (48:14):
Do you have does your door have bars in it
as well? And the average sentence is like sixty seven years? Right?

Speaker 2 (48:21):
Oh, I got a lifetime sentence over here.

Speaker 1 (48:26):
You did a capital discovery. All right, Well, we'll get
to Daniel's interview with physicists Kathy Kopick about what physicists
can do outside of physics. So let's dig into that.
But first, let's take another quick break. All right, we're

(48:54):
asking the question how to think like a physicist? That
sounds like a great T shirt, Think like.

Speaker 2 (49:01):
A physicist, yeah, or a bumper sticker and in.

Speaker 1 (49:04):
The bags is snap like a physicist too? Well, Danny,
you got to talk to another physicist who sort of
does something else that's kind of interesting.

Speaker 2 (49:14):
Yeah. Kathy Kopeik is an old friend of mine. She
and I did experimental particle physics together many years ago,
but then she ventured out into the world instead of
continuing in physics research, and for many years her job
was to help people who have PhDs in physics find
jobs outside of physics, mostly in data science and in
machine learning industry, which has been gobbling up a lot

(49:36):
of physics PhDs.

Speaker 1 (49:37):
Well, she did this for a company or is it
consultant or what.

Speaker 2 (49:41):
Yeah, there was a company called Insight Data Science, which
was like a boot campo take people from physics, give
them a little bit of an introduction into the tools
of business or industry, or at least help them translate
their experience so they knew how to talk about it.
I find that one of the biggest barriers between fields
is just vocabulary. You know, everybody talks about the same
thing and using different words, and so if you just

(50:01):
learn to translate your work, your expertise into somebody else's language,
you can help them understand how you might be useful
to their company.

Speaker 1 (50:09):
Right, Right, You just have to say things like I
worked on a model to understand the universe, and then
all scientists will understand you.

Speaker 2 (50:19):
I'm gonna circle back and connect with stakeholders so that
we can maximize shareholder profit. Right, that's my attempt to.

Speaker 1 (50:26):
Speak corporate world. That's how you think they talk in
corporate America.

Speaker 2 (50:33):
I mean based on the sitcoms I watch, I mean
research I've done.

Speaker 1 (50:36):
Then, yes, is that part of thinking like a physicist
is doing your research on TV and YouTube?

Speaker 2 (50:43):
That's just part of living man.

Speaker 1 (50:47):
Now, you said Kathy used to do that. What does
she do now?

Speaker 2 (50:50):
Yeah? Now Kathy has a bunch of jobs. She's teaching
at Berkeley and at Stanford, and she has her own
consulting company helping people find physicists to work in their teams.

Speaker 1 (50:58):
All right, Well, here is Daniel's interview doctor Kathy Kopeck
on how to think like a physicist and how to
get a job as as a physicist, or how to
pretend you're not a physicist to get a job. Is
that that?

Speaker 2 (51:08):
Yeah, yeah, to get a non physics job if you
are a physicist, there you go. All right. So then
it's my great pleasure to introduce to the podcast my
friend and colleague, doctor Kathy Copik. Kathy, thanks very much
for joining us today.

Speaker 8 (51:23):
Oh, thanks so much. I'm really excited.

Speaker 2 (51:25):
Tell us a little bit about who you are, what
your background is. You have a special and unusual journey.

Speaker 8 (51:31):
Oh yeah, thanks sure. So I was a physicist and
am a physicist. I don't know if we talked in
the past or present tense, but I worked in experimental
particle physicists for a long time, first actually in California
and but Bar, then outside Chicago on the CDs experiment
at Formulab. Then I was at CERN for a long time,

(51:53):
as were you, working on the Atlas experiment with Columbia
and then with Berkeley. So I just I was in
physics for a long time, studying the smallest things, and
then I worked in the last ten years a lot
on helping teams outside of academia think about how they
use data in lots of ways, and how they hire

(52:14):
their teams. I worked for about seven years at the
Insight Data Science Fellows Program, working with a lot of
scientists making a transition from working in science to working
in tech in business, and worked with literally thousands of
people making career transitions to literally hundreds of companies. And
now I work as a consultant field Work partners with

(52:35):
a friend and we help teams do the same kind
of things as consultants.

Speaker 2 (52:40):
So this may seem like an obvious question, but why
are people making a transition. You're getting a PhD in
particle physics, You're studying the secrets of the universe. Why
are people then going to work for healthcare companies or whatever?

Speaker 8 (52:52):
Sure, yeah, I say two main reasons. One is genuine interest.
You know, people are excited about and curious about lots
of things. It's one of the things that drive them
to be scientists in the first place. And I talk
to lots of people who are interviewing with our program
to make that transition, and people were like, you know,
I've done this thing for a long time and I

(53:13):
really like doing it, and now I'm interested in doing
something else. And so I think there is definitely genuine
interest and curiosity about what it's like. And then I
think on the other side, you know, the job market
for academics is very hard getting that next position, that
next position. Both it's very challenging. There's fewer and fewer
positions at every level, and so naturally people have to

(53:35):
exit academia. And also there's often fewer choice, like less
choice of each level, so you know where you're going
to live, what you're going to work on, who you're
going to work with. Getting those positions is pretty tough,
and so not just in physics, but in all fields
across academia. People transition out after their undergrad after their PhD,
after post docs, and sometimes at the faculty level as well.

Speaker 2 (53:58):
So we're always telling our students, hey, come to a
PhD in physics because you're going to learn important skills
about thinking and you're going to train yourself to be
a smart person. And those skills are broadly applicable. And
I've never worked outside of academia, so I don't know
if I've been lying to people. Tell me, have I
been lying to people? What skills do physics PhDs learn
that are actually useful outside of particle physics?

Speaker 8 (54:20):
Sure? Sure, I do not think you are lying to people.
I do think those skills are genuinely useful, and you
can tell when you see where people go on to
work after they've been in physics a lot of times
in physics, and also that's in other places. The skills
that people learn. I think there's three main things. The
first one is just trying to figure out how to

(54:42):
break a problem into smaller problems and questions, thinking about like, Okay,
there's this big question we have, like what's the smallest
thing in the universe, the thing that both you and
I worked on and so have the big question? But
then okay, how do I break that down into things
that can be measured or things that we can write
a theoretic model for. So breaking big questions into small

(55:03):
questions it's a really important skill if you want to
ask questions about the universe, but also if you want
to ask questions about a business, or you know, how
how many beds in a hospital are likely to be
available on a given day given the procedures and things
that are coming up, and how uncertain is it that
people will get discharged on a certain day. If you're

(55:24):
trying to build a model of anything, not just in science,
but also in the real world, breaking a big problem
into small questions is a big, big skill.

Speaker 2 (55:32):
Let me drill into that a little bit. I understand
it's important to know, like how to get started on
a problem. You're working for a company and they give
you this project. They're like, build us this widget that
does that thing, and you need to know what to
do on day one so that after day ninety you're there.
Why is that something that physicists in particular are good at,
Like how does study in the nature of the universe
make you good at learning how to break down problems?

Speaker 8 (55:55):
Yeah, a lot of things that physicists are good at
are think scientists in general are good at asking question
breaking it into problems, But physics in particular, I think
both people who are drawn to physics and physics education
reinforce the same thing, which is not just being a
little bit curious, but being like really curious. You know,

(56:17):
they're not just stopping at some level that's like a
service level or where there's maybe approximations or things you're
like really continuing to either you personally because it's how
you think about the world, or in your education, working
with your teachers and mentors are like really really really
drilling down to these questions, to the really basic pieces

(56:40):
of it. And I think that is unique to physics.
It's you know, the people who study physics have chosen
to kind of like continue down that path of questions
to where you know, there's things are not even alive anymore.
You're studying one atom, or studying how galaxies form, or
some like very complicated basic question about the universe. So

(57:06):
and I think it's true everybody takes a question and
breaks it into smaller questions in science, but in physics
really really trying to get to the most basic things
about how the world works.

Speaker 2 (57:16):
Right, all right, So I interrupted you. You were telling us
the good things that physicists learned, and number one is
breaking things into pieces, and number two.

Speaker 8 (57:23):
Was breaking things into pieces. Number two I think especially
in experimental physics working with very large general purpose data
sets and a lot of parts of science. You know,
every experimental science people have data sets. Sometimes they're very large,
but a lot of scientists create those data sets themselves
in a smaller group, so they have you know, they're

(57:45):
trying to study one thing about how a certain bacteria
does something, or you know, you're in their own lab
and they kind of know, oh, maybe the data from
July is no good because the temperature was off or something.
You know, they know the data often because they created it.
In physics, especially in experimental particle physics, where we both worked,
but also in astrophysics and lots of other areas of physics,

(58:08):
people have these very collaborative general purpose data sets that
are meant not just to answer one question, but you
can ask so many questions from them. And they're messy.
They're built, these detectors that are built, and we have problems,
some parts not working. Maybe that's showing up in some
initial variables, also in some calculated variables down the road.

(58:29):
You have to make corrections. Working with that kind of
general purpose data is a real skill because that real
world data that you might study if you're working at
a business or nonprofit or asking some questions about non
academic data, very similar to So that's a skill I
think people learn in physics. And then a third one

(58:50):
I would say is this collaboration working in. Not all
collaborations are as big as the ones that we worked on.
Most are not, but but working in everybody who's working
in physics and in science is really trying to figure
out what's already been done. Who has domain knowledge that
might help me figure out the piece of it that
I'm working on. How do I share what I'm working

(59:13):
on in a way that can make sense to build
some collaboration. How do I share my results back? How
do I write about and speak about what I learned
in a way that's going to help advance the research
on this question. So all of those I think are
really important.

Speaker 2 (59:28):
So in understanding what it's like to think like a physicist,
I think one thing that's helpful is understanding where physicists
find their skills useful. So you told us the kind
of skills we learn, But where do people who have
been trained in particle physics end up making impacts in
the world outside of particle physics. Where are these skills helpful?

Speaker 1 (59:47):
Yeah?

Speaker 8 (59:48):
I think really everywhere. And I'm not just like trying
to make it seem just everywhere. But in all the
kinds of tech companies that you can think of that
are working today, people are doing interesting work. Also, small places,
nonprofits I mentioned initially. I mentioned this, like people working
at a hospital to try to figure out how to

(01:00:10):
build a system that helps predict when patients are going
to be coming in or not. People are working in pharmaceuticals,
just really in every area I think people are working.
I mean I yeah, there's there's so many ex article
business to know, so many of us that people go
in and people are driven and curious to work on
so many things that Yeah, just lots of places.

Speaker 2 (01:00:33):
And you know, physics is very good broad training, but
we're not learning everything when people go out into the
world and try to work on actual practical problems with
real deliverables and stuff. What are some sort of blind spots.
What are some things that physicists don't learn that are
useful in the rest of the world.

Speaker 8 (01:00:50):
Yeah, I think that all of those advantages, those superpowers
that I talked about have some kind of reverse kryptonite,
which is like being very curious and very detail oriented
and driven to like get to the very bottom of
the question is a good instinct in physics, it's important.
But sometimes in the business world you don't have the

(01:01:10):
time or resources to like get really to the very
bottom of something, and you have to kind of step
back and make an approximation or maybe we're only going
to run this thing for a week and we're going
to get as far as we're going to get, but
at the end, what we're trying to do is like
recommend the next song for someone, or recommend the next
for someone to watch. And so actually it's okay if
like we don't understand everything about this, and so sometimes

(01:01:33):
taking that step back and being like, you know, this
isn't a six month project or a six year project.
This is like a six week project, and we're gonna
build something and we're gonna ship it and it's going
to be good enough for that need, you know. And
there are areas where that's true. And then there are areas,
you know, where like in health and healthcare, where you
don't want to make errors. And so I think people

(01:01:54):
kind of through their personality might choose areas where it's
okay to you know, recommend the next song for someone
they might not enjoy as much. Where it's not okay
to recommend, you know, a medication to someone that's not
the right fit for them, right if it's you know,
and there's still usually in a healthcare setting, there would be
a doctor that would be the prescriber. But if you

(01:02:15):
have a tool that's very biased or making wrong predictions
for something that's really important like healthcare. You know, there's
less room for error.

Speaker 2 (01:02:25):
So you've helped a lot of people figure out how
to go from particle physics to someplace in the real
world where they can make a contribution. How do you
do that? How do you like get to know somebody
and figure out, like what are their strengths and weaknesses
and how does it fit. I mean, you're basically like
the yinta of particle physics and jobs. But tell us
about your process.

Speaker 8 (01:02:44):
Sure, sure, everybody is very different. That's one thing that
I enjoy about it. So you know, some people need
to grow or change in one area, where other folks
that's very different for them. I think the first thing
that I try to ask is what motivates people, what
they're excited by. You know, some people are very excited
by the impact in the real world and the people
that might use or be helped by the thing they're

(01:03:05):
working on. Other folks are very excited about the technical
tools themselves, like getting to use the most advanced tools
and models and getting to work on something technically very exciting.
Other people are have worked very deeply and you know,
worked ten years on one thing and are actually looking
to do something more broad like they're like, oh, I
want to learn about a lot of things. Some people

(01:03:28):
love to interact with a lot of people. Some people
want to be a little bit more like I kind
of want to be given the thing and do my
own thing. And so I think there's very different work
for people depending on what they like and what they're
interested in. And so once you know a little bit
more about that, like what are the constraints around the
kind of jobs that people are looking for, then I

(01:03:48):
think it's easy to recommend specific like okay, well, and
based on geography too, like there's just different kinds of
jobs in different places in North America in the world,
and so okay, well, for you, it sounds like you're
excited about this and you're living here and these are
your experiences. Helping people describe what they've done and what

(01:04:10):
they want to do next. People usually don't need to
build new skills. They have a lot of skills. It's
just they need to have some kind of exploration of
the space of available things, what they want, what they have,
how they can describe what they've done and maybe demonstrate
it in a different way by talking about it differently.

(01:04:35):
Those are the main things I think I would do.

Speaker 2 (01:04:37):
So, I've seen a lot of physicists end up like
on Wall Street or in data science. These seem to
be places like where that community has an appetite for it.
They're like, oh, yeah, we like hiring physicists or whatever. Yeah,
but tell us some other places where physicists might end
up some you know, maybe unusual or bizarre places physics
PhDs end up working in.

Speaker 8 (01:04:58):
Yeah, that's a good question. I do you think people
end up in a lot of places that basically anywhere
where people are like building some models to help a
system run better. So it could be you know, things education,
educational software. People are trying to build ways to help

(01:05:18):
kids learn to read and learn to do math. There's
all kinds of games that people work on. Anything that
you buy or sell clothes or you know, any sort
of products, any sort of recommendations for things that you're
that people are working on. Anything in the healthcare industry.
I talked about that a lot already. Anything in the

(01:05:39):
kind of broad tech you see, there's a ton of
work right now in AI, certainly large language models. A
lot of people from physics are working on those tools
at all the places you can imagine. There's really a
lot of a lot of places. I can't think of
one like fun especially funny, Like, oh, here's one thing

(01:05:59):
you can think of. But in every area media, fashion,
people are working in all sorts of areas.

Speaker 2 (01:06:05):
People working on like optimizing you know, underwear sizes and
stuff like this.

Speaker 8 (01:06:09):
For sure, for sure, that's particle physics at work. That's right,
it's funny and it's a joke. But it's also true
that like I don't know, for me, finding clothes that
fit is actually really nice.

Speaker 2 (01:06:21):
Yes, it's an important, unsolved fart. You can make a
real impact in people's daily lives.

Speaker 8 (01:06:25):
I mean, it's like a little bit silly, But it's
also true that there's a lot of I think there's
a lot of systems where people have just done the
same thing forever and having a fresh take on it
can be helpful.

Speaker 2 (01:06:35):
Yeah, everybody's got like their favorite pair of jeans or
their favorite pair of underwear, and there's a reason they
fit right, it feels good. So there's this lore going
around that I hear a lot that one of the
reasons behind the two thousand and eight financial collapse was
that Wall Street went a little bit crazy with its modeling,
and that there were these crazy quants and that most

(01:06:56):
of them were ex physicists who didn't really understand the
system and just like wrote a bunch of code that
went crazy and destroyed people's lives. So would they have
to say that did just call the cause the financial
collapse or not?

Speaker 8 (01:07:11):
Probably not alone. I'll say that the I do you
think there's a superpower kryptonite that physicists are very interested in,
you know, going down to the root causes the basic
how do you take this problem break it into the
basic parts? And I think that the cryptonite version of

(01:07:32):
that is like thinking that you can do that in
any field, for any topic without necessarily consulting and learning
about the domaining knowledge of the practitioners or people that
have worked in that area. There's a famous data science person,
Drew Conway used to say, physicists where like kind of
like wil to beasts that would like run into an
area that seems interesting, like biophysics, right, It's like, oh,

(01:07:55):
there's something interesting there. All the physics here comes a
lot of old you know, ex physicists who are like,
we'll solve all the problems. And so when I would
give talks to physics. I would say, don't be a
will to beast, like, don't run into their area to
a new area. So these maybe these two thousand and
eight physicists are kind of just like I know, I'll
break down this problem into these parts and look what
I'm doing, isn't it cool? But if there is a

(01:08:17):
little bit more domain knowledge or thought around, how could
this go wrong? How might this affect people who? Why
might we not do this? They could have avoided some
bad outcomes?

Speaker 2 (01:08:32):
All right, So maybe we're not totally guilty, just partially.

Speaker 8 (01:08:35):
Yeah.

Speaker 2 (01:08:36):
So a lot of our audience are folks who like
physics and like thinking about physics and have been listening
to the pod and learning to think like a physicist
and applying you know, that mental model to questions about
the universe. But what would be your advice for somebody
out there who wants to take advantage of this way
of thinking, somebody who's not necessarily trained as a physicist
but wants to learn to think like a physicist. What

(01:08:58):
would be your advice for learn need to think that way?

Speaker 8 (01:09:01):
Yeah? I think there's this. I'm sure you talk about
the Drake's equation, which is used for thinking about where
extraterrestrial life might be in the Milky Way?

Speaker 2 (01:09:10):
Right?

Speaker 8 (01:09:10):
Is that right? Probably know much more of that. So
that's the thing where you kind of are taking these pieces.
Anybody can look up the Drake equation or Drake's equation
and taking these pieces and trying to put it together
to get one answer. And I went to a business
class where people were talking about using the same sort
of thing to model businesses or other processes where it's

(01:09:31):
just trying to think about anybody can think about what
are the parts that come together to create some answer
or some prediction. And so just take thinking about that.
Breaking something up into things that you can measure individually
or you can think about individually, can really help solve
a problem, whether it's a science problem, business problem, any

(01:09:53):
kind of problems.

Speaker 2 (01:09:53):
All right, So then last question, a bit of a
personal one. What do you miss most about actively working
in physics? Say about being a physicist, because I think
you're always a physicist once you're trying, like once a Jedi,
always in Jedi. But what do you miss most about
like working on particle physics other than working with me?

Speaker 8 (01:10:10):
Obviously I was gonna say, I mean, you're joking, but
I think I really really did. There's a very special, fun,
exciting environment of being at the lab in these big
experiments at both that you know, at Slack in California,
Fermi Lab, Brooke Caven Cern that these labs just it's

(01:10:35):
really literally people from all over the world and having
lunch together and the big cafeteria. Cerns called our one
restaurant one a very creative name. I don't know if
it still is. It's not named after someone now, is
it still our one? Yeah? So our one. So if
you're there for lunch or for coffee or the end
of the day, it's just really fun to run into

(01:10:57):
so many people then you've worked with over your whole career,
people who are getting into the field, people who are
very senior. You never know who's going to be there.
Just having some food, drinking coffee and getting to talk
to people about what they're working on and also what
they're doing and how they are. It's very very fun
memories of hanging out there with all sorts of people,

(01:11:21):
and yeah, no, it was a great time. So I
would say just missing being with all the people that
we used to work with and getting to meet new people.
That's a really truly international environment too, really fun.

Speaker 2 (01:11:35):
It is fun to hear conversations in so many different languages.
I like running into the same really old Nobel Prize
winners over and over again, introducing myself every single time
because they don't remember me because they're like one hundred
and fifty years old. And I also remember one of
the first times I was at our one and you
had some special trick for making an iced coffee. When
you showed it to meet and Katrina and for the

(01:11:56):
rest of the summer we were like, oh, let's get
a Kathy. We called it a kathy, you know, a coppuccino.

Speaker 8 (01:12:01):
That's yeah, yeah, I've made up created a TGI Fridays.
I don't know if you're looking for sponsorships Stan TGI
Fridays the restaurant when I was a server there, created
the Copacina Cocino delicious.

Speaker 2 (01:12:15):
Thank you got us through that summer.

Speaker 8 (01:12:17):
Yeah, they don't have They didn't have cappuccino machines. That's
sort They had espresso machines, but no, no cappuccino machines,
so you got to figure it out all right.

Speaker 2 (01:12:25):
Well, thanks very much for sharing with us how to
think like a physicist, and how to drink coffee like
a physicist. Really appreciate it.

Speaker 8 (01:12:32):
We'll put the put the recipe people.

Speaker 2 (01:12:35):
In the show notes for Copucina show notes. All right,
thanks Kathy.

Speaker 1 (01:12:41):
All Right, interesting talk there, Daniel. It seems like she's
basically saying we all have skills.

Speaker 2 (01:12:48):
Everybody's skills are different at least. Yeah. I think she
probably aligns with you to think that, like scientists are
all curious thinkers and mental model builders, and not even
all physicists are the same. We all think differently and
enjoy different parts of the process. Hmm.

Speaker 1 (01:13:03):
And that can help you get a job, right, because
these are all skills that we could all use in
every field.

Speaker 2 (01:13:08):
Probably, yeah, exactly. And so in the end, thinking like
a physicist is just like thinking like a scientist, being
a curious person, trying to understand the world, being methodical
about it, try not to fool yourself with what the
data is telling you.

Speaker 1 (01:13:20):
Yeah, and just trying to maximize your functionality to the stakeholders.

Speaker 2 (01:13:26):
Exactly.

Speaker 1 (01:13:27):
Maximize shareholder revenue, maximize physicists employment.

Speaker 2 (01:13:35):
Try not to cause any more financial collapses. Please.

Speaker 1 (01:13:37):
All right, Well, an interesting discussion about thinking like a scientist,
thinking like a physicist, what are the commonalities and how
things might be a little bit unique. For people who
pursue physics as a career.

Speaker 2 (01:13:49):
And for those of you out there not pursuing physics
as a career, but who have discovered a love for physics,
keep doing it, keep thinking like a physicist or a scientist,
and keep being curious about the world and trying to
make the whole thing click together in your mind.

Speaker 1 (01:14:03):
Yeah, but mostly just keep thinking, please.

Speaker 2 (01:14:06):
And keep listening to the pod.

Speaker 1 (01:14:08):
Thanks everybody, We hope you enjoyed that. Thanks for joining us.
See you next time.

Speaker 2 (01:14:17):
For more science and curiosity, come find us on social
media where we answer questions and post videos. We're on Twitter, Discorg, Insta,
and now TikTok. Thanks for listening and remember that Daniel
and Jorge Explain the Universe is a production of iHeartRadio.
For more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts,

(01:14:38):
or wherever you listen to your favorite shows.