
Cause and Effect Fallacy: The By-product Does Not Make The Product - jayliew
http://times.jayliew.com/2012/10/12/cause-and-effect-fallacy-the-by-product-does-not-make-the-product/#.UHjkEWk-uYU
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arocks
I expected a better response. The author seems to be confusing a _successful_
product with a _great_ product (which Zach was trying to say). Both are sadly
different goals.

One could have a mediocre product that generates a lot of revenue through
better marketing. However, I don't think that would be what someone like Zach
(or many hackers) would like to work on.

The right combination of team, product, and market is fundamental to many
businesses. However, in technology products the right team makes a lot of
difference compared to other industries. A good team can grow only in a good
culture. Hence the importance of both.

I honestly don't think that the Cause Effect post was meant to be feel-good.
It actually addressed the pain points of what's wrong with most dysfunctional
companies and has good suggestions for improving the situation.

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jayliew
Where is it that Zach implied that what he's talking about there is only
applicable if you've decided to build a "great" product, but if you're aiming
to build just a "mediocre", "successful" (but not "great"), then what he's
saying is completely irrelevant and not applicable to you?

I realize you don't speak for him, but I'm curious if I legitimately
misunderstood something here.

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manmal
Calling "Cause and Effect Fallacy" on some slides without having proof at hand
is just as fallacious. Both are opinion pieces. Please don't give something a
"scientific spin" if you are just stating yet another opinion with anecdotal
evidence at best.

There should be another fallacy name for exactly what this article does.

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jayliew
<http://en.wikipedia.org/wiki/Physics_envy>

Especially with social sciences, there's this thing called "physics envy". In
physics, experiments can be repeated consistently in a pristine lab-like
environment gather lots of data, to then to prove and disprove stuff, at
marginal cost. It's many times impossible to do in other areas of science.

Nobody has yet been able to definitely prove a theorem, but it does not mean
that existing hard won lessons today should be ignored until they can be
"proved". Startups have to make decisions with incomplete data all the time.

