Sometimes there's a fine line between data cleaning and cherry-picking your data.
My new favorite example of this is based on something Nassim Nicholas Taleb said at a talk at Penn (starting at 32 minutes in): that 92% of all kurtosis for silver in the last 40 years of trading could be traced to a single day; 83% of stock market kurtosis could also be traced to one day in 40 years.
One day in forty years is about 1/14,600 of all data. Such a disproportionate effect might lead some "outlier hunters" to discard that one data point. After all, there are many data butchers (not scientists if they do this) who create arbitrary rules for outlier detection (say, more than four standard deviations away from the mean) and use them without thinking.
In the NNT case, however, that would be counterproductive: the whole point of measuring kurtosis (or, in his argument, the problem that kurtosis is not measurable in any practical way) is to hedge against risk correctly. Underestimating kurtosis will create ineffective hedges, so disposing of the "outlier" will undermine the whole point of the estimation.
In a recent research project I removed one data point from the analysis, deeming it an outlier. But I didn't do it because it was four standard deviations from the mean alone. I found it because it did show an aggregate behavior that was five standard deviations higher than the mean. Then I examined the disaggregate data and confirmed that this was anomalous behavior: the experimental subject had clicked several times on links and immediately clicked back, not even looking at the linked page. This temporally disaggregate behavior, not the aggregate measure of total clicks, was the reason why I deemed the datum an outlier, and excluded it from analysis.
Data cleaning is an important step in data analysis. We should take care to ensure that it's done correctly.