
Think like a statistician, without the math - morisy
http://flowingdata.com/2010/03/04/think-like-a-statistician-without-the-math/
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joe_the_user
Hmm,

Statistics is already dangerous enough with the math, I shudder to think of it
without the math.

\--> The danger just begins with the umpteen media-driven conclusions driven
by the correlations that one can find in data while neglecting the point that
correlation does not imply causation. The danger continues with the
fallacious-but-Nobel-prize-winning efforts of Black and Scholes (see collapsed
American economy...). Maybe "thinking like a statistician without the maths"
means understanding these problems but I see nothing in the article to
suggestion this. The article seems to describe the process as "attend to
details, think big picture, live-right, think-right, to-thine-own-self-be-
true, etc" (nothing but intellectual banalities).

Fooled by Randomness is not a great book but it might be a much better place
to start than this article. Still, I would say, learn the maths, darn it.

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physcab
I think people are missing the point of this article. The author isn't saying
that people without math degrees should go out and try to be statisticians.
What he's really saying is that anyone with the right mindset can do
statistical analysis. I believe this to be 100% true. If you are in the type
of position that looks at a lot of data, you will naturally develop an eye for
trends and outliers. Then if you are curious enough, you might draft up some
conclusions which you then bring to attention to others. They will say, so how
does this compare to the population as a whole? So you go back and look
through the numbers. Even if you don't have a background in math, you're lucky
that you've got Google. You type in "how to handle outliers" or "how to
analyze trends in data". You learn about the basics (ie normalization,
standard deviation, means, percentages etc). Then you go back and look at your
data again. It's a continual process of discovery and one that you can learn
through application.

For the record, I really like FlowingData.com. There have been some awesome
posts, particularly the one about creating a U.S County thematic map.

~~~
joe_the_user
_If you are in the type of position that looks at a lot of data, you will
naturally develop an eye for trends and outliers._

So what do you base your "eye" on? A lot of good statistics involves debunking
the trends that people with an "eye" read into the data without them really
being there. The human is a fantastic instrument for seeing patterns - it just
has the small problem that it can easily see patterns in entirely random data
- especially patterns with complex, non-normal distributions (lot of fractal
random distributions are great for seeming like they were produced
intentionally but without maths, how would you know that?).

It's true that _sometimes_ when you see a trend, it really is there -
_sometimes_. What differentiates a _good_ statistician from a crank or snake-
oil salesman is that they use a rigorous method to sift the trends they see.

Just consider that the "Eliot Wave Theory" is huge, utterly bogus theoretical
method based on "seeing patterns" of stock market movement that can't be found
by rigorous methods. We don't actually need more of that kind of thing.

~~~
physcab
> So what do you base your "eye" on?

This article refers to everyday people who might look at "ordinary" data.
We're not talking about government climatologists or financial analysts here.
We're talking about people in Sales and Marketing who might want to know the
demographics of their customers, perhaps by location for example. So in this
case, I mean quite literally, use what you see in front of you to identify
anomalies. Maybe you notice that the age bracket 18-24 year olds seems
significantly higher than the rest of the population.

I don't want to discredit the need for statisticians. I'm doing my PhD
research on machine learning techniques. But in most practical scenarios, you
don't need to resort to such a rigorous analysis. You can get quite far just
using run-of-the-mill techniques (mean, standard deviation, normalization,
what have you)

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jpwagner
Think like a chef, without the food

Think like a chauffeur, without the car

Think like a pediatrician, without the child

Think like a n00b, without the inexperience

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lmkg
I agree with this a lot. Math and statistics are just as valuable to
individuals for the style of thinking and discipline, as they are for any
particular result. Just like the scientific method useful in any discipline
making decisions based on evidence, even if you never need to drudge up those
chemistry or physics equations.

My degree is in mathematics. I'm an analyst so I work with numbers a lot, but
I use nearly none of my "math background" in my line of work. I don't deal
with limits or topologies or rings or primality or anything. Nonetheless, I
still feel that my math background helps me a lot. Any time you're in the real
world you have to work with intuition and experience and make a boatload of
assumptions, and that's a perfectly reasonable way to make decisions if you're
good at it. Math's precise, rigorous reasoning helps you challenge the
soundness of your assumptions and your intuition, which is invaluable.

~~~
joe_the_user
My degree is in math too.

I think the article is bogus and kind of dangerous.

Math is a matter of exact calculation. If the axioms are true, the premises
will follow. Statistics seems like math but involves a group of hidden
assumptions about "the world". It's crucial to know the difference between the
two fields.

Like mathematics, statistics offers an array of machinery producing results.
Unlike mathematicians, statistician should be "gate keepers" as well as
technicians. Ideally, a statistician should be there to say "just because you
_can_ your stock market data into a heat equation, it doesn't mean you
_should_ plug your stock market data in a heat equation".

Unfortunately, the article seems to entirely neglect this aspect except in
banalities that a non-math person would be entirely incapable of applying
("dig deeper" won't help you if you don't know why normal distributions matter
and why not everything is normal).

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macromicro
Program your support vector machines from an R package! Make your graphs in
excel! Have only the most fundamental concept of what a relational database
is!

All that, and you still won't know what you're doing to your errors when you
take a logarithm.

~~~
physcab
If you know enough to take the logarithm of something, you will undoubtedly
know what its doing to your errors as well.

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RyanMcGreal
Just what we need, cargo cult statistics.

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roundsquare
This article should really be entitled: "Non math things to remember when
doing the math" or "Think like a statistician while forgetting stuff you
learned in your first statistics class."

These tips are useful if _you are doing math_. But, I think anyone doing the
math would (should?) know that outliers can be important and not to read their
own agendas into things.

Statistics without math is really about asking "how did you test for this? Is
that the appropriate way to check for this? What about this explanation? Is
the causality correct here?" Its not _doing_ statistics without math (which
you can't do) but "questioning* statistics without doing the math (which you
can, sometimes do).

One alternate title: "Some things you learn in introductory classes aren't
very useful in the real world."

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kvs
Data, data everywhere
[http://www.economist.com/specialreports/displayStory.cfm?sto...](http://www.economist.com/specialreports/displayStory.cfm?story_id=15557443)

Might be a relevant read too.

