satt points out that (via the Bienaymé formula) “An RCT with a sample size of e.g. 400 would still be 10 times better than 4 self-experiments by this metric.”

Since this has come up again, I may as well point out that this is a very abstruse argument.
* First of all, if the standard error in a random variable is low to begin with, or I've already done many experiments, decreasing the standard error of my estimate by a factor of 10 is much less valuable.
* And second of all, this analysis doesn't connect with anything actionable. What does decreasing the standard error of my estimate by a factor of 10 even mean in actionable terms? How often will this actually end up changing what I do?

The way I'm thinking about this argument is to picture a normal distribution representing my uncertainty about some value. When I do 100 times as many experiments, the distribution
* becomes skinnier by a factor of 10, and
* centers itself at a new location, where the probability of the new location is determined by the original distribution. If my original distribution is especially wide, more experiments could be valuable, especially if the new distribution ends up jumping somewhere far from the center of the original distribution. But if my original distribution was plenty skinny to begin with, making it skinnier won't help me.

See also this comment of mine, which does math showing that just a few perfectly done self-experiments can be quite valuable in actionable terms: http://lesswrong.com/lw/bs0/knowledge_value_knowledge_quality_domain/6d9k