Welcome back to The Deep Dive. Today we're plunging
into something really fundamental, that little
question we all ask. What would happen if? It's
like the spark behind everything, isn't it? From
discovering fire way back when to, I don't know,
modern science? It just drives us. It really
does, that core curiosity. So for this deep dive,
we've been reading this fantastic article. It's
by Gary Ackerman over on Hackscience .Education,
and the title is... Perfect. What would happen
if? Simple title, but packs a punch. Totally.
And it's not just about answering the question
itself. It really challenges us to think about
how we get to those answers, and maybe more importantly,
what kind of hidden stuff might be getting in
the way. And that's really our goal for you today,
isn't it? To kind of unpack how that really simple
question leads down some pretty complicated roads.
We'll touch on the tools we're supposed to use.
You know, observation. logic, controlling things.
The scientific basics. Exactly. But then there's
the tricky part, the big one, bias and how hard
it is to actually reduce it. We want to give
you maybe a shortcut, a way to see those hidden
traps that shape what we believe, even when we
feel totally objective, like seeing the blueprint
of how we think and noticing the little cracks.
OK, yeah, let's dig into that. So Ackerman starts
right at the foundation, right? The core of science.
Talk about observation, control, logic, all aimed
at understanding nature, figuring out cause and
effect, attributing changes to what we think
caused them. Right. And these aren't just, you
know, dusty old concepts for a lab coat. They're
meant to be practical tools. Observation forces
you to just look first before jumping to conclusions.
Like really look. Yeah. And control, well, that
helps isolate what's really making the difference.
Cutting out the noise. Stripping away other factors.
Exactly. Yeah. And logic. That's about making
sure your argument actually holds water step
-by -step. That A really leads to B, based on
the evidence. What's fascinating is how these
are basically our first line of defense against
the biases we're going to talk about. They're
designed to lead us to reliable conclusions.
Right. Designed to. But even with that solid
foundation, Ackerman points out this huge challenge.
Uh -huh, the bias part. Yeah, he calls it, perhaps
the most difficult principle of science to follow,
the constant effort to identify and reduce bias.
And honestly, reading that, it really, it made
me think, can we ever fully get rid of it? even
with the best methods. Well, that's the million
-dollar question, isn't it? And Ackerman explains
why it's so tough. We're usually just unaware.
We don't see our own biases. Or end spots. Total
blind spots. We think we're seeing clearly, making
rational choices. But these biases are there,
shaping everything. It's like trying to find
your keys in the dark when you think you know
where they are. Yeah. Good analogy. He gives
a really clear example. Racist and sexist language.
He points out that once you identify that language,
Once it's challenged, you can consciously work
on reducing it. But here's the really interesting
bit. It's not just about saying less offensive
things, which is vital, obviously. Of course.
It's that by forcing yourself to change the language,
the output, you actually start to challenge the
underlying bias itself. Ah, OK. So tackling the
symptom affects the cause. Precisely. It creates
this, like, cognitive distance, you have to reevaluate
the thoughts, the automatic assumptions that
produce that language in the first place. Yeah.
It forces kind of internal correction. Wow. But
it brings up that huge question, right? How do
you even start fighting something if you don't
know it's there, if you can't even perceive its
influence? Yeah, that's the core difficulty.
Ackerman takes this idea of bias into a slightly
different, maybe more subtle area, particularly
in professional settings. He talks about our
preferred methods of thinking and acting. Oh,
OK. Not just the big societal biases, but our
personal preferences. Exactly. It's less about
overt prejudice and more about how we fall in
love with certain ideas or ways of doing things,
especially new, exciting ones. Right. He uses
that great analogy, the shiny object. Yes. Like
a new teaching method or maybe a new management
strategy or even just a new app someone raves
about. It catches our eye, it promises great
things. And the trap, the psychological trap
is that once we believe it's going to work. We
start looking for proof that it is working. Exactly.
We become predisposed to see confirmation. Our
brain starts filtering everything. We notice
the successes, maybe explain away the failures.
Or attribute failures to something else entirely.
Right. We might even sort of unconsciously steer
things to get the result we want, just because
we're so invested. It's like, you know, that
new diet. Oh yeah, I know this one. You start
it, you're convinced it's the answer. For two
weeks, every little thing feels like proof. You
feel lighter, more energy. Totally. Confirmation
everywhere. Then, maybe a few months later...
You're kind of back where you started, wondering
what happened. That initial belief just colored
all the data coming in. That is such a perfect
example. We've all done that, haven't we, with
diets or work projects or tech gadgets. And this
brings us to Ackerman's really critical point,
the one that shakes things up. Once we believe
the method will work, then any data that we collect
will confirm our bias. That statement just directly
challenges the whole idea that we can collect
objective data once we're already sold on something.
It really does. But doesn't that create a problem?
Like, how do we ever embrace genuine innovation
if we're so suspicious of our own enthusiasm?
Can we become too cynical? That is the Balancing
Act. Absolutely. It's not about shutting down
new ideas entirely. It's more about adopting
maybe skeptical optimism. Skeptical optimism.
I like that. Acknowledge the potential, but maintain
critical distance. Ackerman isn't saying new
is always bad. He's questioning the unquestioning
acceptance. And that leads to his critique of,
say, data -driven educators. But really, it applies
everywhere. Anyone claiming their decisions are
based purely on data. Right. Data -driven decisions
are huge now. Huge. But Ackerman suggests they
might be fooling themselves and the constituents
if that data is just confirming a preexisting
belief. So the data isn't driving the decision.
The decision is selecting the data. Potentially,
yes. If you connect this to the bigger picture,
think about marketing campaigns, policy decisions,
business strategies, any field where numbers
are used to back up a strong conviction. We tend
to find the data that supports what we want to
be true or what we already think is true. And
the danger is, well, bad decisions, wasted effort,
lost trust when the data -backed plan fails.
It's an echo chamber disguised as evidence. And
this leads to what I thought was maybe the most
powerful line in the article, that inversion.
The opinion shaping the facts. Exactly. Instead
of saying they're detailing the facts upon which
they base their opinions, Ackerman suggests they
might actually be detailing the opinions upon
which they base their facts. And that just flips
everything, doesn't it? It makes you question
the entire process, not just the outcome. Are
we finding truth or just confirming our opinions?
It's a profound shift in perspective. It forces
you to confront that uncomfortable idea. Maybe
our beliefs aren't always the logical endpoint
of analyzing facts. Maybe, often, the belief
comes first, the preference, the shiny object,
and that dictates which facts we notice, how
we weigh them, even how we look for them. It
challenges that ideal of pure objectivity we
strive for. And look, this isn't just some abstract
philosophical point. It has real consequences.
How so? Well, think about public debates. People
cling to ideas, even with contradictory evidence,
by just dismissing or twisting the data, right?
It happens all the time. Or, in companies, teams
might stick with outdated methods they like,
finding data points to justify it, instead of
genuinely evaluating effectiveness. It's a constant
battle against how we're wired. So, wrapping
this up then, what's the big takeaway for you
listening? This whole deep dive into Ackerman's
article, it really highlights this ongoing, often
invisible fight against bias. Even when we try
to be scientific data -driven, our own preferences,
those shiny objects, can subtly pull us towards
confirming what we already think instead of discovering
something new. It's a reminder that understanding
isn't just about gathering information, but also
about constantly checking the lens we're using
to see it. It really is. And considering Ackerman's
background, his books on technology and schools
management. He's clearly thought a lot about
how this works in practice. It makes you wonder,
how can you, in your work, in your life, get
better at spotting your own biases? When you
see that new method, that convincing argument,
that shiny object, can you pause and ask, what
are the opinions here that might be shaping how
I see the facts? Maybe try actively looking for
stuff that contradicts what you initially think.
Ooh, that's uncomfortable. It is. Ask, what if
I'm wrong? Before you ask, how can I prove I'm
right? It's not easy. It's a continuous process
of, like, self -awareness. Really powerful questions
to ask ourselves. Maybe just start noticing,
you know? Notice when the data seems to line
up perfectly with what you hoped for. Right.
Just pause and question it. Was it objective
discovery, or was it perhaps finding facts to
fit an opinion, building that muscle, that vigilance
seems crucial? Well, thank you for joining us
on this deep dive into critical thinking bias
and the slippery nature of objectivity. Always
a pleasure. Until next time, keep asking, well,
what happened if, but maybe, just as importantly,
keep asking, how do I really know?