So I want you to picture a scenario. Just think
about a typical day at work. OK. Or maybe you're
just at home troubleshooting a weird noise in
your car. Yeah. You're staring at the engine,
trying to figure out the problem, and suddenly
you find an explanation. Right. It fits perfectly.
It makes complete sense. It explains the noise.
Or if you're at work, it explains the drop in
sales. Whatever it is, what is the very next
thing you do? Well, usually. You stop looking.
You stop looking. You totally stop. You pat yourself
on the back, you close the ticket, and you just
move on. Yeah, it's that moment where you stop
being an investigator and you kind of start being
a defense attorney for your own idea. Exactly.
And that is exactly what we are unpacking in
today's Deep Dive. We're looking at a source
that argues that specific moment, that feeling
of, aha, I found it, is actually a massive cognitive
trap. It really is. And the source we have for
this is fascinating. It's a bit of a... A source
within a source, actually. Yeah, it's layered.
We're looking at a blog post by Gary Ackerman,
published in December 2025 on hackscience .education.
It's titled On Multiple Working Hypotheses. But
the backstory here is what's really wild. Ackerman
shares this personal story about a paper he read
as an undergrad back in 1985. Right, and it completely
changed his life. It did, but then he lost his
copy of it. He spent 40 years looking for this
specific paper. 40 years? Yeah, and he finally
tracked it down recently using AI to search for
the concepts. And the kicker, the paper wasn't
from 1985 at all. No, it was from 1890. 1890,
written by a geologist named T .C. Chamberlain.
I just love the archaeology of this idea. We
were dusting off this 135 year old document to
answer a really modern question. Which is basically
why the standard scientific method and, you know,
the one we all learned in fifth grade might actually
be terrible for our critical thinking. It's a
really bold claim. I mean we're usually taught
that forming a hypothesis and then testing it
is the absolute peak of intellectual rigor. Right,
but Chamberlain argues it's actually a step down
from the real gold standard and Ackerman does
a great job breaking down Chamberlain's core
framework. Yeah, let's get into that framework
because Chamberlain identified three distinct
types of learning or three ways we try to discover
new truths. Right. And the first one he points
out is what he calls the ruling theory. Yeah,
the ruling theory. This is essentially the bottom
of the ladder. It's dogma. Just strict imitation.
Exactly. You accept an explanation simply because
an authority figure, a teacher, a boss, some
ruling voice told you it's the truth. You aren't
investigating anything. You're just complying.
It sounds a lot like the because I said so style
of management. It totally is. And the danger
is that you start interpreting any new facts
specifically to make them fit into that old theory
you were handed. Which is efficient, I guess.
but it's completely stagnant. You never learn
anything new. Right. So eventually, thinkers
realized this wasn't great, and they moved to
the second tier, which Chamberlain calls the
working hypothesis. And this is the one most
of us are familiar with. You see a problem, you
come up with a likely explanation, your one working
hypothesis, and then you test it to see if it's
true. That's the one. But this is where I got
a bit stuck when I was first reading the source
material, because that sounds, well, correct.
That's how science works, right? You have an
idea and you test it. It seems logical. So why
does Chamberlain and Ackerman, by extension,
think this method is actually dangerous? It's
all about human psychology, really. Chamberlain
had this incredible psychological insight way
ahead of his time. He argues that the flaw isn't
in the testing, it's in when you stop thinking.
What do you mean? Well, as soon as you formulate
that single working hypothesis to answer the
question, how did this happen, your mindset shifts.
You stop being an impartial observer. Because
now I have like... skin in the game. Exactly.
You're invested. Chamberlain uses this brilliant,
honestly kind of funny phrase for it. He says
the moment you create a single theory, it becomes
your intellectual offspring. Intellectual offspring.
Oh, man. So we basically turned into helicopter
parents for our own ideas. We do. A sort of parental
affection kicks in. And because of that affection,
even if you are actively trying to remain objective,
you unconsciously start steering the investigation.
Make your kid look good. Right. You start cherry
picking data that supports your idea. Yeah. And
maybe even worse, you naturally start to ignore
or downplay evidence that supports any other
theory. I can completely see that happening.
It's so relatable. If I go to my boss and say,
hey, I think the problem with our sales is the
new marketing budget. Now I am highly motivated
to find evidence that proves it's the marketing
budget. Yes. You press the facts to fit your
theory, just like a parent making excuses for
their child's bad behavior at the playground.
Like, oh, he didn't mean to push that kid. He's
just expressing his boundary. Exactly. The data
isn't an outlier. It's just a special case. That
is so true. So the working hypothesis doesn't
just guide your research. It actively biases
you as the researcher. You're no longer testing
to find the truth. You're testing to prove yourself
right. It's a subtle distinction, but it's massive
when it comes to critical thinking. OK, so if
having one idea is a trap, what's the alternative?
Do we just go in with zero ideas? No, you go
in with the third tier, the method of multiple
working hypotheses. Multiple working hypotheses.
OK, so that just sounds like having backup plans.
It's a bit more profound than that. It's a fundamental
shift in your starting line. Instead of coming
up with one explanation and testing it, you force
yourself to explain and test every rational explanation
you can conjure up all simultaneously. Wow. You
don't just have one child. You bring a whole
family of intellectual offspring into the world
at the exact same time. I see. So if I have five
hypotheses, I can't really play favorites. Right.
If you have five kids, you have to be fair. It
neutralizes your bias because your affection
is divided. Let me try an analogy here to see
if I'm tracking the difference between the single
working hypothesis and the multiple method. It
feels a bit like the difference between betting
on a horse and just watching the race. Oh, I
like that. Go on. If I'm using the single working
hypothesis, I'm basically putting all my money
on one horse. Let's call him Glacier. OK, Glacier.
I'm watching the race, but I'm only watching
Glacier. If he stumbles, I make excuses for the
track conditions. If he speeds up, I'm cheering.
But I don't really care what the other horses
are doing unless they get in his way. My observation
is entirely narrowed to that one lane. That's
a perfect way to put it. You're blind to the
rest of the field. Right. But with multiple working
hypotheses, I haven't placed a bet or I guess
I've placed an equal bet on every single horse
in the race. So I'm just watching the whole field
to see who is actually the fastest. And that
allows you to see the race as it truly is. rather
than how you want it to be. You're a judge, not
a gambler. It's a powerful reframing. And it's
not even about being smarter than everyone else.
It's just a hack to trick your own ego. Exactly.
And we have to remember the context here. Chamberlain
wasn't just sitting in a room thinking about
abstract philosophy or horse racing. He was a
working geologist. Right, in 1890. Yes. So he
was dealing with massive, incredibly complex
natural systems. Specifically, he uses the example
of the formation of the Great Lakes. The source
material mentions this. I imagine in the late
1800s, there were some pretty heated debates
about how those legs got there. Oh, incredibly
heated. One camp of geologists would say, glaciers
carve them out. Another camp would say, no, it
was ancient river erosion. And if you're using
the single working hypothesis, you pick your
side, you pick glaciers, and you go out looking
for glacial scratches on the rocks. And guess
what? You will absolutely find them. Because
they're there. And then you declare victory.
See? I told you it was the glaciers. But you
completely missed the fact that the river erosion
is also there. Chamberlain points out that in
the real world, especially with complex phenomena,
there is almost never just one simple cause.
Right. The Great Lakes were formed by glaciers
and pre -existing erosion and tectonic shifts,
all of them interacting together over time. So
having a single hypothesis forces this weird
true false binary onto a world that is actually
like all of the above. Exactly. That is the danger
he's worrying us about. If you are looking for
the cause, singular, you will definitely find
a cause. But you'll miss the whole system. Which
means your solution is going to be flawed. Right.
The method of multiple working hypotheses forces
you to look at the interaction of causes. Chamberlain
said this leads to better and truer observations
because you simply aren't forcing the data to
fit a neat little narrative. That makes so much
sense. And this transitions perfectly into the
second half of our deep dive today. Because Gary
Ackerman, the guy who wrote the 2025 blog post,
he's not a geologist. No, his field is education.
Right. And he argues that a modern classroom
is just as complex, if not more complex, than
the geology of the Great Lakes. Oh, without a
doubt. Think about it. 30 distinct personalities
in a room, different backgrounds, different learning
styles, different moods on any given day, all
interacting in real time. It's an ecosystem.
It is. And Ackerman points out that the intellectual
offspring trap hits the education sector incredibly
hard. How does that play out in the school setting?
Well, look at the debates teachers and administrators
constantly have. They're always searching for
the best method. Oh, yeah. Like, is it better
to use tablets or traditional textbooks? Exactly.
Is it better to lecture, or should everything
be project -based group work? Is phonics the
best way to teach reading, or is it whole language?
You're constantly looking for the ruling theory,
that one silver bullet that's going to fix everyone's
test scores. And Ackerman says that is fundamentally
the wrong question to ask. If you apply Chamberlain's
multiple working hypotheses to teaching, you
stop asking, what is the best method? And what
do you ask instead? You start asking, what are
the special values of these different methods?
Oh, wow. What are the special values? That is
a huge shift in perspective. It changes a teacher's
entire toolkit. Instead of trying to prove that
tablets are the future of learning, you hold
multiple hypotheses at once. You acknowledge
that tablets help with interactive engagement,
but textbooks help with sustained focus, and
lectures are great for providing structure. You
keep all those options open and active. Right.
So when a student is struggling, you aren't stuck
trying to force your pet theory to work. You're
not sitting there saying, well, my tablet method
is the best, so if this kid isn't learning, the
kid must be the problem. Exactly, you have a
full array of reasons why a lesson might work
or might fail. Ackerman writes that when educators
understand this full array, they are so much
more likely to find a solution that actually
helps the student in front of them. It prevents
you from becoming a hammer that only sees nails.
Yes. It stops you from being rigid. And honestly,
this applies to way more than just teaching or
geology. Oh, absolutely. Troubleshooting code,
resolving a fight with your spouse, figuring
out business strategy. If your business is failing,
you shouldn't just say, well, the market is down.
Right. You have to ask, is the market down? Or
is my product outdated? Or is my marketing just
boring? You have to test all of them. I love
this concept. I really do. But I have to play
devil's advocate for a second here. Go for it.
This sounds completely exhausting. It is. It
really is. I mean, just cognitively. Human brains
crave certainty. We want to close the open loops.
If I'm in a management meeting and I propose
five conflicting reasons why we're losing money
and I say we need to investigate all of them
equally, people are going to look at me like
I'm crazy or at least indecisive. You're not
wrong. And Chamberlain actually knew this would
be a hard sell. He was very upfront about the
challenges of this method. And he highlighted
two big downsides. Okay, what's the first one?
Communication. It is incredibly difficult to
express multiple complex, conflicting ideas at
the same time to a general audience. Because
people just want the bottom line. Yes. They want
clarity. They don't want a messy list of possibilities.
They want a hero and a villain. They want the
one true cause. It's complicated. is rarely a
winning campaign slogan. Exactly. And the second
challenge he noted is what he called the problem
of the uninitiated. Meaning people who are new
to a topic. Right. He worried that young students
or adults who are completely unfamiliar with
a specific field would just get overwhelmed by
the cognitive load. That makes sense. If I don't
know the basics of geology and you start telling
me it's a complex mix of glaciers, rivers, tectonics,
and maybe a meteor strike, I'm just going to
check out. Right. Chamberlain thought that maybe
people need to master the basics of the simple
ruling theories before they can attempt this
kind of high wire act of holding multiple hypotheses.
So there's a real risk of analysis paralysis.
If you keep every option open all the time, maybe
you never actually learn or decide anything.
That was his fear in 1890. But interestingly,
Ackerman brings in a counterpoint in his blog
post from a more modern scientific heavyweight.
Steven J. Gould. Ah, the evolutionary biologist.
I remember reading some of his essays years ago.
Yes. Gould wrote extensively for Natural History
Magazine, and Ackerman points out that Gould
fundamentally believed we underestimate the public.
Really? Yeah. Gould believed that lay readers
are perfectly capable of understanding highly
sophisticated, nuanced topics. So Gould's basically
saying, don't dumb it down. Essentially, yes.
And Ackerman seems to side with Gould on this.
He suggests that the reason people struggle with
complexity today isn't because their brains can't
handle it, it's because we don't teach them how
to handle it. We feed people. ruling theories
and single hypotheses for so long that their
complexity muscles just completely atrophy. That's
a great way to put it. That is quite a condemning
thought, actually. We train people from childhood
to look for the one simple answer, and then as
adults, we wonder why they can't handle nuance
in politics or science. And in the age of AI
and endless information overload, that inability
to handle nuance is genuinely dangerous. If you
stop at the first answer that feels good your
first intellectual offspring, you are very likely
stopping at a half -truth. It's so interesting
to think about Ackerman's own journey with this.
He spends 40 years looking for this one paper
because he remembered it being life -changing.
When he finally unearths it, does he feel it
still holds up today? He absolutely does. In
fact, he argues it is more relevant now than
it was in 1890. And there is a real poetry in
the fact that he used AI to find it. How so?
Well, think about what AI is. It's the ultimate
answer engine. You type in a prompt, and it gives
you an answer. Usually just one definitive answer.
Exactly. And if we aren't careful, we just treat
that AI output as our new ruling theory. We accept
it because the machine authority gave it to us.
Ackerman is using this 135 -year -old paper To
remind us that we have to be the ones generating
the multiple hypotheses Otherwise, we're just
outsourcing our critical thinking to an algorithm
an algorithm that might just be Hallucinating
its own single hypothesis anyway Wow, so bring
this all back together. We have a geology paper
from the 1890s teaching us a vital lesson about
modern education AI and just daily critical thinking
and the core lesson really seems to be about
humility. Scientific humility. It's having the
bravery to say, I have a really great idea, but
I'm going to deliberately look for reasons why
I might be wrong, and I'm going to actively look
for three other explanations at the exact same
time. It's about being willing to kill your darlings.
Or, to use Chamberlain's amazing phrase, you
have to be willing to neglect your intellectual
offspring. You do, for the greater good of finding
the actual truth. It's tainful, it goes against
our nature, but it's entirely necessary. So here
is the challenge we want to leave you with today.
We always try to give you something practical
to do with this information. The next time you
find yourself entirely sure that you know the
answer to something, whether it's why your car
won't start or why a major project at work failed
or even why a friend is mad at you, I want you
to just stop. Catch yourself right in that moment
of absolute certainty. Ask yourself, am I just
protecting my pet theory right now? And then
force yourself to write down at least two other
possible explanations, even if they sound totally
unlikely at first. Just the physical act of writing
them down breaks the spell of the single hypothesis.
It might hurt your ego a little bit, but it might
also be the only way to actually solve the problem
in front of you. Thank you so much for diving
deep with us today. Keep looking for the alternative.
Keep asking why and what else. See you next time.