Problems with Outlier Detection In a blog post Andrew Gelman writes:

Stepwise regression is one of these things, like outlier detection and
  pie charts, which appear to be popular among non-statisticians but are
  considered by statisticians to be a bit of a joke.

I understand the reference to pie charts, but why is outlier detection looked down upon by statisticians according to Gelman?  Is it just that it might cause people to over-prune their data?
 A: @Jerome Baum's comment is spot on. To bring the Gelman quote here:

Outlier detection can be a good thing. The problem is that
  non-statisticians seem to like to latch on to the word “outlier”
  without trying to think at all about the process that creates the
  outlier, also some textbooks have rules that look stupid to
  statisticians such as myself, rules such as labeling something as an
  outlier if it more than some number of sd’s from the median, or
  whatever. The concept of an outlier is useful but I think it requires
  context—if you label something as an outlier, you want to try to get
  some sense of why you think that.

To add a little bit more, how about we first define outlier. Try to do so rigorously without referring to anything visual like "looks like it's far away from other points". It's actually quite hard.
I'd say that an outlier is a point that is highly unlikely given a model of how points are generated. In most situations, people don't actually have a model of how the points are generated, or if they do it is so over-simplified as to be wrong much of the time. So, as Andrew says, people will do things like assume that some kind of Gaussian process is generating points and so if a point is more than a certain number of SD's from the mean, it's an outlier. Mathematically convenient, not so principled.
And we haven't even gotten into what people do with outliers once they are identified. Most people want to throw these inconvenient points away, for example. In many cases, it's the outliers that lead to breakthroughs and discoveries, not the non-outliers!
There's a lot of ad-hoc'ery in outlier detection, as practiced by non-statisticians, and Andrew is uncomfortable with that.
A: This demonstrates the classic tug of war between the two types of objectives for statistical analyses such as regression: descriptive vs. predictive. (Pardon the generalizations in my comments below.)
From the statistician's point of view, description usually matters more than prediction. Hence, they are inherently "biased" towards explanation. Why is there an outlier? Is it really an error in data-entry (extra zeros at the end of a value) or is it a valid data point which just happens to be extreme? These are important questions for a statistician.
OTOH, the data scientists are more interested in prediction rather than description. Their objective is to develop a strong model that does a great job of predicting a future outcome (e.g., purchase, attrition). If there's an extreme value in one of the fields, a data scientist would happily cap that value (to the 98th percentile value, for instance) if that helps improve the predictive accuracy of the model.
I don't have a general inclination towards either one of these two approaches. However, whether the methods/approaches such as stepwise-regression and outlier-treatment are "a bit of a joke" or not depends on which side of the fence you are standing.
