> Larger datasets will always lead to more statistical confidence I think I understand what you're trying to say, but I don't agree with how you've phrased it.

> Larger datasets will always lead to more statistical confidence I think I understand what you're trying to say, but I don't agree with how you've phrased it. More data can lower p-values or increase power for a given analysis, but the assumptions that go into using data don't change when you simply have more of it. And those assumptions are everything in statistics. In fact, I think the temptation of 'more is better' leads to more use of what is easily available which can be highly biased. You also get much more 'significant' results that are more tempting to believe in. It's harder to shy away from a very low p-value, even when you know the sampling may not be appropriate. I'm a biologist, and as a field we have a lot of issues to address with bioinformatics. Over-eager investigators will pull out interesting tidbits from datasets without considering how problematic that sort of hypothesis generation can be. Fortunately the grant reviewers seem to be well aware of this for the most part. I've heard of many people having grants rejected because they thought they found a 'one in a million' phenomenon, but it turns out they looked a million times to find it :) Good bioinformatics is still firmly grounded in genetics.