Comment by siyer | Hacker News Reader

Comment by siyer | Hacker News Reader

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This is just reflexive HN negativity. As a pain researcher who's completely unaffiliated with the group that did this work, I find this to be an excellent paper - carefully done, with good controls, standard sample sizes for the field, and sensible assays.

People interested in more of the details can read the Nature Methods article here: http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth...

It also fits into a long history of work out of the Mogil lab about the limitations in our current assays for pain. It's worth noting that this is by no means an easy problem. Pain is inherently subjective, and measuring and quantifying it in humans is difficult, let alone in rodents. The only way forward is more work like this - that carefully quantifies the sources of potential bias and error in our assays, and proposes solutions for how to get around them.

Edited to add: Vox has a good interview with Mogil that goes more into this. You can read it here: http://www.vox.com/2014/4/28/5653444/mice-get-stressed-out-f...

I'll take the liberty of rephrasing jerf's comment: this is the sort of paper I'll be more likely to believe after it's been reproduced by multiple labs. Until then, I'd just as soon not even be told about it.

It sounds too much like any number of "xxx affected by cell phone radiation!!1!!" results that historically have given rise to much panic, intense discussion, and clicking of links, but have never, ever, ever proven to be consistently replicable. At some point, skepticism and even a bit of "HN negativity," as you put it, becomes the most rational response to initial reports of such phenomena.

My point is more that there are differing levels of the prior probability of a study being true. Yes, 'xxx affected by cell phones' stories are bunk, however, those stories tend not to be published in Nature Methods, tend not to come out of highly respected laboratories, and tend not to have voluminous documentation and results, with 9 Supplementary Figures.

I've been reading this paper closely over the past 30 minutes, it's important enough that I'm sure it'll be heavily discussed at conferences and meetings this year. I can't think of a single thing that these researchers could have done that they haven't done. They've approached the question carefully, looked into a ton of second-order explanations and effects, and provided a plausible discussion of how and why they see their effects.

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