> "Data is data and data is good" This is a reasonable intuition, but is hard to square with the increased risk of publication bias for small studies (i.e. we

> "Data is data and data is good" This is a reasonable intuition, but is hard to square with the increased risk of publication bias for small studies (i.e. we should question whether the typical small study published is really "data" in the sense we'd like). Since small studies are more sensitive to noise and "fishing" for an effect, and since non-results are rarely published, what happens with meta-analysis of many published small studies is that you end up primarily looking at outliers rather than typical results. Since most research questions have a directionality to what counts as "interesting", the outliers also tend to be clustered on one side rather than evenly split. The two papers I recommend anyone read if they care about these problems are: Why Most Published Research Findings are False http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124 http://journals.plos.org/plosmedicine/article?id=10.1371/jou... The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf http://www.stat.columbia.edu/~gelman/research/unpublished/p_...