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Social Science Journal 'Bans' Use of p-values
sandbagger writes: Editors of Basic and Applied Social Psychology announced in a February editorial that researchers who submit studies for publication would not be allowed to use common statistical methods, including p-values. While p-values are routinely misused in scientific literature, many researchers who understand its proper role are upset about the ban. Biostatistician Steven Goodman said, "This might be a case in which the cure is worse than the disease. The goal should be the intelligent use of statistics. If the journal is going to take away a tool, however misused, they need to substitute it with something more meaningful."
It is the job of the reviewer to check that the statistic was used ion the proper context. not to check the result, but the methodology. It sounds like social journal simply either have bad reviewer or sucks at methodology.
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This is social science. Mathematics and statistics aren't even relevant.
Yes they are. Get quantitative data, use quantitative methods.
Just because most social 'scientists' are not experts at statistical inference, it doesn't mean it can't be done correctly.
p-values are just a probability of something. Do you experiment well and 'something' makes sense.
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At least one president of the American Psychological Association published a statistics book intelligent enough that it used to be required in university statistics intro classes: http://books.google.com/books/about/How_to_use_and_misuse_statistics.html
Not that he would have disagreed with the comment about social psychologists...
I agree with you. Yet no need for the quotes around social 'scientists.' Psychologists, socialists, etc. employ the same experimental designs and mathematical techniques in experiments as doctors or others performing drug efficacy or medical outcome experiments, for example.
P-Value: It's intervention versus control group. Standard, basic scientific experimental design and statistical analysis stuff.
It's an uninformed and naive view to think that people looking at the behavior of humans at the level of social organization are somehow intellectually or scientifically less able than those examining them at the biological level.
I could cite examples of your folly all day, but since only one instance is needed to refute your foolhardy blanket statement, this will suffice:
http://en.wikipedia.org/wiki/I...
Wow! Would [apart from a self-aware and non-self-righteous human being] could imagine that the processes of the brain could be measured and analyzed mathematically?!
Well, I guess you learn something every day, eh?
I don't think you even need to be pushing people to do Bayesian stats. You just need to force them to graph their data properly. In *a lot* of biological and social science sub-fields it's standard practice to show your raw data only in the form of a table and the results of stats tests only in the form of a table. They aren't used to looking at graphs and raw data. You can hide a lot of terrible stuff that way, like weird outliers. Things would likely improve immediately in these fields if they banned tables and forced researchers to produce box plots (ideally with overlaid jittered raw data), histograms, overlaid 95% confidence intervals corresponding to their stats tests, etc, etc.
Having seen some of these people work, it's clear that many of them never make these plots in the first place. All they do is look at lists of numbers in summary tables. They have no clue in the first place what their data really look like, and know good knowledge of how to properly analyse data and make graphs. Before they even teach stats to undergrads they should be making them learn to plot data and read graphs. It's obvious most of them can't even do that.
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This is social science. Mathematics and statistics aren't even relevant.
Undergraduate psychology and sociology students are required to study statistics. Undergraduates in medicine, biology, chemistry, physics, et al. are not. So, perhaps you need to rethink your ignorance about the limits of the scientific method and educate yourself about what these subject areas actually entail in real life instead of in your uninformed world view.
Actually, p-values are about CORRELATION. Maybe *you* aren't well-positioned to be denigrating others as not statistical experts.
I may be responding to a troll here, but, no, the GP is correct. P-values are about probability. They're often used in the context of evaluating a correlation, but they needn't be. Specifically, p-values specify the probability that the observed statistical result (which may be a correlation) could be a result of random selection of a particularly bad sample. Good sampling techniques can't eliminate the possibility that your random sample just happens to be non-representative, and the p value measures the probability that this has happened. A p value of 0.05 means that there's a 5% chance that your results are bogus in this particular way.
The problem with p values is that they only describe one way that the experiment could have gone wrong, but people interpret them to mean overall confidence -- or, even worse -- significance of the result, when they really only describe confidence that the sample wasn't biased due to bad luck in random sampling. It could have been biased because the sampling methodology wasn't good. I could have been meaningless because it finds an effect which is real, but negligibly small. It be meaningless because the experiment was just badly constructed and didn't measure what it thought it was measuring. There could be lots and lots of other problems.
There's nothing inherently wrong with p values, but people tend to believe they mean far more than they do.
Note to ACs: I usually delete AC replies without reading them. If you want to talk to me, log in.
, in the real world they are simply a way of inserting subjective intuition into numerical judgements.
That is actually one of the selling points. You're going to insert subjective intuition into your judgements and methods regardless of what method you use. With proper use of Bayesian methods you can more explicitly state your assumptions, even if you don't do much about them.