If the sample size
was too small,
the luck of the draw could've skewed the results.
We should test this on 10,000 people but...
I only know ten.
That's okay, making friends is hard.
(Adam)
Or maybe there was a confounding variable.
Like, if the subject was
using another product
that could affect the results
and the researchers didn't account for it.
Oh, wow, this pill is really
boosting our subject's resistance to sunburn.
(Adam) And something, very rarely,
unscrupulous researchers will fake their results.
What if we just make up numbers
that show that this pill
cures cancer?
Hello? Nobel Prize committee?
I've got some data you've gotta see.
Sure, so we just need a way to find out
which studies are accurate.
We already have one. And that's to reproduce them.
To show the results
weren't just some fluke,
someone else
has to do another study
that tests the same question
to see if they can get the same findings.
It's like double checking the answers.
Curse, my results were way different.
And here's the problem.
Since studies are usually reproduced
after they're published,
that means they have a chance
to influence other research or even become famous
before we find out they're wrong.
For instance, remember that famous study on power posing.
Uh, it's only one of the most watched TED Talks of all time.
It proved that by standing in the power pose,
your body produces hormones that give you confidence.
Well, no one's ever been able to reproduce those results.
What?
Or how about that series of studies
from the '70s and '80s
that showed you can actually make yourself feel happier
just by smiling?
Well, that makes sense, right?
Sure, except that in 2016, almost 20 labs tried
to replicate it and not one of them
was able to reproduce those findings.
(train whistle blows)
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