[dopu]

I think it's a process that's best learned by watching other scientists do it. That's how I've learned to do so. Though you're never done -- you can always improve your bullshit detectors.

There's always obvious things to catch: do all of the data points plotted on this graph make sense? Does a predictive model fall back to chance performance when it should? Should the authors need to z-score in order to see this effect? Other labs typically use X statistical model here: why are these people using something different, and how should I expect the results to change? If my brain is having a difficult time understanding how they went from raw data to a given plot, that's usually an indication that something's fishy.

Then you need to check for consistency between the results. Figures should come together to tell a coherent story. If there are small discrepancies in what Figure 1 and Figure 3 are telling you, investigate. It could be that the authors are hiding something.

Then there's sociological factors: how much do I trust the work that this group puts out? Being in higher-prestige journals is actually an indication that you should be more skeptical of the results, not less. There's more riding on the results telling a neat and coherent story, and more space to hide things in (given that you typically need to do 2 or 3 papers worth of experiments to publish in Nature these days).

Unfortunately, there's a great deal of bullshit published every day. Every paper has a little bit of it. The trick is to get great at spotting it and getting something out of the paper without getting discouaged with science as a whole.