This is definitely true and I also agree that one shouldn't be snobbish about popular culture. That said, it's also true that you can have your cake and eat it: there's plenty of good music that also has good tunes.

I don't think "pop music" is a genre, I think it encompasses multiple genres. So on the one hand, by virtue of being a catch-all term, pop is varied. On the other hand, mainstream popular music must now grab the listener as quickly as possible on the first listen. To achieve this new songs must have a lot of familiar content and they must be simple. This pushes mainstream popular music towards less variety and less depth. Thus I don't think pop music is the most varied of all genres: it's mostly recycled candy. If you want the three course meal you need to look elsewhere.

Central to this is the assumption that "psychological" means "made up". It doesn't have to the be that way. Consider classical conditioning of Pavlov's dogs: bell ringing predicts food and eventually bell ringing on its own causes salivation of dog. Is that psychological or physical? Is this just a semantic difference? The conditioning is underpinned by a real pathway in the brain that's been created and/or strengthened: so that's a physical thing. But ultimately you only know this has happened by watching the behavior of the animal: which is closer to "psychological". They're both two sides of the same coin.

What if chronic fatigue syndrome is somehow like that? What if somehow these patients have a condition where any exertion triggers a strong and abnormal fatigue response and this leads to a vicious circle wherein they get more tired with progressively less activity. That would explain why the condition is so resistant to treatment and why it has no obvious measurable hallmarks other than fatigue.

Working in a research lab, I feel that ratio is pretty far off. It's a lot more than 2 weeks. If stuff goes badly, maybe you have to chuck out 3/4 of your experimental work. If stuff goes well, all your experimental work goes into your thesis.

Go to the flat earth society forum and engage with them in honest discussion as this pompous twat did. You'll be surprised what they believe.

It probably won't get their attention: they are blind to reason. They will just think the film is propaganda and either not watch it or do so but use twisted logic to disregard it. People believe the Earth is flat, so believing vaccines are bad for you is a far easier delusion to maintain. The only thing that might change their minds is if their kids die or are debilitated by the diseases they are failing to vaccinate them against. Their current belief system has little of any obvious repercussions on themselves, so it's easy to continue the self deception.

That doesn't stop the individual from looking at Cochran reviews and so forth to get a more balanced picture and making up their own minds. I do this whenever the media bring up a new study making some claim or other.

So it's still not quite as good as KDE 3, then? The good news is that it seems to be getting there. Probably around 3 or 4 years to go at this rate of progress.

This is a dangerous and foolish attitude to take. The threats facing humanity raised by the "greens" -- your chosen term not mine -- are real, quantifiable, and ongoing and will not go away because a particular milestone has not been reached when predicted. There are crystal clear and very worrying trends across a range of domains such as climate, deforestation, availability of fresh water, insect populations, desertification, and pollution. Brushing them aside because the "apocalypse" hasn't happened yet is beyond silly.

On reflection, I take back the raw data statement (which was made WRT to lack of data points overlaid onto error bars -- a common thing in the field). There ought to be more detailed analyses of what individual bees do.

Based on what I see in the field in general, I wouldn't say n=14 is "tiny". I would say it's "OK could be better" but of course that depends on SD and effect size too. As for the lack of raw data points, that's inexcusable (i.e. there is no reason not to do it) but unfortunately very common indeed in this field. I've found from experience that you can't write off a paper just because they miss out the raw data points: you'd throw out 90% of the field and it wouldn't make sense. FWIW, authors are obliged to send a reader raw data upon request. I've not really read the paper, but based on the figures it seems they lack controls of the sort you mention. That's the problem. The rest I can live with.

If they did that, it's gross data manipulation. You don't know that they did that and there is no evidence that they did.

I agree a larger n would be nice (say, n=30 at least) and I *think* it's likely not too hard to obtain in this case. I would caution, however, that sometimes it's a lot harder than it looks to obtain these data. It could be that n=14 is hard to do.

I used to work in insect neuroscience and I collaborated with people who did experiments of the general sort described in the paper. The issue was of course not finding insects -- we had lots of insects -- the problem we had was that running the experiments was very time consuming and could often fail for unclear reasons. You may get drift in behavioral scores over time, batches of insects that produce suspect results, etc. All sorts of really weird stuff happens with animal behavior and so to get solid results you believe in might require throwing out most of your data (e.g. because variance was weirdly high on some days). After all is said and done your sample size isn't always what you hope for. I've seen really good people work for years and still end up with sample size of less than 10 animals.

It is true that memorisation could explain this. You point about the sample size is trickier, though. Firstly there is no magic number that constitutes a large (vs small) sample size. What is suitable depends on the size of the effect, the variance, and the degree to which you want to generalise the results to a wider population. This is often balanced against what is possible. In biology a lot of experiments have a small sample size because of the cost or difficulty in gathering the data. For instance, I just reviewed a paper where the authors have gathered data from just a single subject. However, they gather a vast amount and do a very thorough job. Their work still stands as it is (it's in a sense a methods paper) and given that they aren't targetting a big name journal or over-selling their results I'm going to let the n=1 slide.

In the particular case of this paper, what I find most annoying isn't the n=14 but that their graphs hide the underlying data by displaying them as just bars with a 95% confidence interval for the mean. I would also agree, however, that I don't see why in this case they couldn't have produced a larger sample size. That's not the main issue, IMHO, however.