Timeline for Reliability of single case reports vs group inference
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| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
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| Sep 18, 2016 at 11:51 | comment | added | amoeba | @wildetudor The difference is that null hypotheses (that are rejected in statistics) make some probabilistic claims about how some properties are distributed in the population. Whereas hypotheses that can be disproved with N=1 are "absolute". Take "there are no talking dogs": the distribution of talking ability among dogs is hypothesized to be a delta function at zero, and so it takes N=1 to disprove it. In contrast, a null hypothesis "adults dogs mean weight is 10 kg" assumes a nontrivial distribution of weights across dogs; you need a large N to be able to say if the mean is 10 or not. | |
| Sep 18, 2016 at 11:38 | vote | accept | z8080 | ||
| Sep 18, 2016 at 11:37 | comment | added | z8080 | Putting aside logistics/ethics of whether samples of N>1 can/should be collected, can we say that a falsification obtained with (say) N=20 is more reliable/believable than one with N=1. Would it make any sense to say that the former is a statistical falsification and the latter merely a conceptual one? | |
| Sep 18, 2016 at 11:37 | comment | added | z8080 | Thanks for this helpful answer. It is your 3rd paragraph that my question alluded to, and which best answers it, i.e. the report of N=1 talking dogs is enough to disprove the assumption "there are no talking dogs". It's still interesting to note that hypothesis tests, that require sample sizes of N>1, have nevertheless the same aim of disproving (falsifying) something - namely, the null hypothesis. | |
| Sep 18, 2016 at 11:13 | history | answered | Tim | CC BY-SA 3.0 |