Take the cold-fusion debacle. When independent groups attempted to replicate the results they were unable to do so.
Of course the other big take away is that people really don't understand statistics at a basic level, even the "scientific reporters".
Agreed. Too bad funding agencies rarely if ever give you the money to do it :(.
The takeaway here is that statistics is arguably one of the most nuanced quantitative fields out there. It's really easy to shoot yourself in the foot, particularly with p-values.
I think every statistical test has its place, but my personal favorite lambasting of p-value testing is Steiger and Fouladi's 1997 paper on non-centrality interval estimation [1].
As an aside, Steiger was my graduate statistics professor several years ago, and probably the primary reason I know this paper even exists. If you enjoy the harsh treatment of significance testing in the paper, just imagine hearing it straight from the horse's mouth during lecture :).
[1]: http://www.statpower.net/Steiger%20Biblio/Steiger&Fouladi97....
That way they can turn weak correlations into strong causations, rake in the eyeballs, and then go "oops" whenever someone points out problems.
Besides, it's more fun to do a meta-analysis on a flock of sorta-vaguely-similar studies.
Even if I had the time to read several studies and expertise to understand them, I couldn't afford it. Out of curiosity I just went to the Journal of Nutrition (I know nothing about it, picked that journal randomly) and tried to download one random issue. It was $273. Going to multiple original sources isn't practical for most of us.
I doubt that the typical mainstream journalist writing a popular story on a scientific subject could justify this expense to her boss either.
I'm all for open access, because I think everyone should have the right to see publicly funded scientific research. But from my experience as a journalist who's written a number of pieces on scientific works it often isn't a big problem. I never had to pay for a journal article to do my work.
Most journals also show you abstracts for free, which are overviews of what each paper says. If you find a paper that you want to read in full, email the author - most scientists are very happy to know that people are interested in their work, so they'll give out copies to anyone who asks.
This isn't to defend the paywalls - I think open access is important. But I don't think the paywalls are a valid excuse for journalists.
http://www.pnas.org/content/111/26/9425.full
Not to say that many/most papers can't be found with a little bit of effort, and I can't really imagine a situation where an establishment journalist emails authors about one of their papers and doesn't get it.
> For me, the takeaway from this affair is that there is no one-size-fits-all solution to make statistics impossible to hack. Getting rid of p-values is appropriate sometimes, but not other times. Demanding large sample sizes is appropriate sometimes, but not other times. Not trusting silly conclusions like “chocolate causes weight loss” works sometimes but not other times. At the end of the day, you have to actually know what you’re doing. Also, try to read more than one study.