Timeline for What are common statistical sins?
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| Dec 7, 2022 at 12:54 | history | edited | User1865345 | CC BY-SA 4.0 |
added 50 characters in body
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| Mar 9, 2012 at 5:31 | comment | added | probabilityislogic | @rolando2 - You still want a likelihood ratio, but when there is a substantive value that has meaning, you should really be testing on this value rather than on $0$. So if $|\beta|=1$ has physical meaning in terms of the coefficient, then you should really be testing for $|\beta|>1$ vs $|\beta|\leq 1$. And not $\beta=0$ vs $\beta\neq 0$ | |
| Jan 6, 2011 at 23:18 | comment | added | Mike Lawrence | I guess the use of LRs vs CIs will likely vary according to the context, which may be usefully summarized as follows: More exploratory stages of science, where theories are roughly characterized by the existence/absence of phenomena, may prefer LRs to quantify evidence. On the other hand, CIs may be preferred in more advanced stages of science, where theories are sufficiently refined to permit nuanced prediction including ranges of expected effects or, conversely, when different ranges of effect magnitudes support different theories. Finally, predictions generated from any model need CIs. | |
| Jan 6, 2011 at 23:11 | comment | added | Mike Lawrence | Ah, so by effect size, you mean absolute effect size, a value that is meaningless unto itself, but that can be made meaningful by transformation into relative effect size (by dividing by some measure of variability, as I mentioned), or by computing a confidence interval for the absolute effect size. My argument above applies to the merits of LRs vs relative effect sizes. There may utility to computing effect CIs in cases where the actual value of the effect is of interest (eg. prediction), but I still stand by the LR as a more intuitive scale for talking about evidence for/against effects. | |
| Jan 6, 2011 at 20:22 | comment | added | rolando2 | Mike - You've got me interested, but do your points extend to effect sizes as simple as mean differences between groups? These can be easily interpreted by a lay person and can also be assigned confidence intervals. | |
| Jan 6, 2011 at 19:54 | comment | added | Mike Lawrence | I'm actually unclear as to how a likelihood ratio (LR) does not achieve everything that an effect size achieves, while also employing an easily interpretable scale (the data contains X times more evidence for Y than for Z). An effect size is usually just some form of ratio of explained to unexplained variability, and (in the nested case) the LR is the ratio of unexplained variability between a model that has an effect and one that doesn't. Shouldn't there at least be a strong correlation between effect size and LR, and if so, what is lost by moving to the likelihood ratio scale? | |
| S Jan 6, 2011 at 15:28 | history | answered | rolando2 | CC BY-SA 2.5 | |
| S Jan 6, 2011 at 15:28 | history | made wiki | Post Made Community Wiki |