348 karma · joined August 27, 2009
I'm not sure why you are saying science claims not to know about telepathy. Every scientist I know would claim it doesn't exist because it's theoretically impossible and empirically never been observed.
"Replication is nearly unheard of?" Are you kidding? Read a journal. Seriously. You're citing popular news articles as if you understand anything about the field.
Oh, and I'm still waiting for my picture of gravity. But not here, because this thread is no longer worth my time to check on.
The problem with comparing p-values between psychology and particle physics is statistical. In psychology, there may be 2 groups of 30 participants being examined with a crucial manipulation. So there's ONE Fisher-type statistical comparison with a relatively few degrees of freedom (because there aren't millions of subjects). In particle physics, they make millions of comparisons (same with genetics studies, etc.) which, by random chance, will result in many statistically significant findings at the 5% level. So they use incredibly low p-values to restrict these Type 1 errors, and still have frequent unreplicable, false findings (see http://jama.jamanetwork.com/article.aspx?articleid=181647). Discussing p-values as if smaller p-values == better science is incorrect, and does not reflect either field or their statistical contexts accurately.
Read this article. It's a model that makes testable predictions about cognition. It's one of tens of thousands. http://groups.psych.northwestern.edu/gentner/papers/Gentner8...
There are real things to consider about the study of the mind, predisposition and plasticity, dynamic systems versus symbolic computation, etc., and you're comparing p-values between particle physics and studies with human subjects? Armchair theorizing about autism? Calling all of psychology (which spans more sub-fields than one might imagine) a psuedoscience? Disappointing, to say the least.
p.s. The DSM is written by psychiatrists.
Also, I don't see the problem of lack of independence. People are still randomly assigned to one of the conditions in the MAB scheme, people in one sample don't affect people in the other sample, and each person submits one independent datapoint. The problem is just one of unequal sample sizes in your two conditions, so when you make the comparison (in a one factor ANOVA, say), you lose lots of statistical power because your effective sample size is essentially the harmonic mean of the two sample sizes (which is heavily biased towards the lower number of the two).
Might want to re-title your article then.
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