I'm not necessarily too worried about that. Statistical significance is the wrong concept for QS and its use is essentially cargo cult statistics.

Leaving aside the profound conceptual and applied problems with null-hypothesis testing ( http://lesswrong.com/lw/g13/against_nhst/ ), QS is much closer to cost-benefit analysis where effect sizes and costs are the critical variables, not alpha. We don't care about testing some intervention and not making the completely arbitrary cutoff of 0.05 (which doesn't mean anything about the truth of the hypothesis in the first place)! We care whether the intervention make a large impact on the variable in question and how expensive the intervention was; if, say, the intervention is an expensive supplement that costs hundreds of dollars a year, we want a higher burden of proof than if the intervention is something free (like taking your vitamin D supplement in the morning rather than evening) or something we should be doing anyway (like exercise).

Far* more worrisome than QS's failure to run t-tests and ritually chant 'we calculate a p-value of <0.05 therefore we reject the null hypothesis of no difference' is the pervasive publication bias (who reports failed experiments?), the absence of blinding even where quite easy leading to severe placebo effects (many supplements), tiny sample sizes, and dodgy data collection (selection bias).

* If you are wondering why anyone would care about my opinion, I've been self-experimenting for years and have a little bit of insight into the matter; see http://www.gwern.net/Zeo http://www.gwern.net/Nootropics and http://www.gwern.net/Weather

https://personalexperiments.org/article/about

EDIT: bleh, and now that I look at my original comment, I see I failed to escape an asterisk so the formatting is completely screwed up.