This is probably meant in the sense of (statistical) power analysis, but I am not entirely sure given the absence of further context.

See

http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12306/f...
"‘Will my study answer my research question?’ is the most fundamental question a researcher can ask when designing a study, yet when phrased in statistical terms – ‘What is the power of my study?’ or ‘How precise will my parameter estimate be?’ – few researchers in ecology and evolution (EE) try to answer it (e.g. Taborsky 2010). Consequently many, possibly most, studies are underpowered (Jennions & Møller 2003; Smith, Hardy & Gammell 2011) and likely to be uninformative or misleading (Ioannidis 2005). Failure to consider power can also result in overpowered studies. Both under- and overpowering waste resources and can raise ethical concerns (e.g. in animal studies, by potentially causing needless suffering; and in disease control by causing potentially promising control methods to be prematurely dismissed). Hence, researchers should take all reasonable steps to ensure sufficient, but not wastefully excessive, power."

https://www.bio.org/advocacy/letters/clinical-trials-bio-sub...
"Theoretically, an incorrect assumption could also lead to an overpowering of a study. We are not clear as to why it is implied that underpowering is the only concern."

https://www.ma.utexas.edu/users/mks/statmistakes/UnderOverPo...

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Note added at 1 hr (2014-12-27 14:16:05 GMT)
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See also blog and discussion at http://theness.com/neurologicablog/index.php/p-hacking-and-o...

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Note added at 1 hr (2014-12-27 14:21:53 GMT)
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In your example referred to in the discussion, the primary analysis was performed for an overly/unnecessarily large sample/population (number of cases), which is why the treatment variance/difference for/of Xxxxx XXX XX was overestimated.