[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
"Wallace, John" <John_Wallace@affymetrix.com> |

To |
"'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: logarithmic scales |

Date |
Mon, 1 Dec 2003 11:41:03 -0800 |

One place where tiny p-values are important is in multiple-comparison tests, where you're applying some sort of Bonferroni-like correction or venturing into False Discovery rates and the like. If you stack enough tests on top of one another in a given analysis, its likely you'll meet that p-value cutoff of significance purely by chance... I've seen a fair number of presentations (powerpoint-"disabled" to boot) with lecturers implying the thermonuclear detonation of the null hypothesis (thanks for the image, Nick). It seems to me that in such instances rather than going after the null with such bloodthirsty vigour a better use of time would be spent looking at the alternative hypotheses (i.e., the variable accounts for no more than N% of the variance in the population measured) -JW -----Original Message----- From: Roger Newson [mailto:roger.newson@kcl.ac.uk] Sent: Monday, December 01, 2003 8:43 AM To: statalist@hsphsun2.harvard.edu Subject: RE: st: logarithmic scales At 15:31 01/12/03 +0000, Nick Cox wrote: >I'd assert, perhaps very rashly, that beyond >some threshold, very low P-values are >practically indistinguishable. I suppose that >log P-value of -20 is often appealing as a kind of >thermonuclear demolition of a null hypothesis, but I wonder >if anyone would think differently of (say) -6. Also, >as is well known, the further you go out into >the tail the more you depend on everything being >as it be (model assumptions, data without >measurement error, numerical analysis...). >On the other hand, there are situations >in which an overwhelming P-value is needed >for any ensuing decision. A good discussion of this issue is given in Subsection 35.7 of Kirkwood and Sterne (2003), which is a basic text aimed mostly at non-mathematicians. This uses a Bayesian heuristic, based on the well-known result that the posterior odds between 2 hypotheses after the data analysis is equal to the prior odds between the same 2 hypotheses multiplied by the likelihood ratio between the 2 hypotheses. It is argued that a P-value below 0.003 is good enough for most of the people most of the time, because, *if* the prior odds are as bad as 100:1 against a nonzero population difference, *and* the power to detect a difference significant at P<=0.001 is as low as 0.5, *then* the posterior odds in favour of a nonzero population difference, given a P-value <=0.001, will be 5:1 in favour. This heuristic seems to make sense to me, if the P-value is for the parameter of prior interest in the study design protocol, because not many grant-awarding bodies will pay for a study for which they consider the prior odds of an interesting difference to be worse than 100:1 against. On the other hand, in the real world, with today's technology, it is nearly always cheaper to torture the data until they confess than to collect more data. Therefore, a lot of people's colleagues expect them to do "subset analyses from hell", and are reluctant to write up negative results as such. Therefore, an honest scientist who wants to accumulate publications is often not a data miner, but a "data lawyer", cross-examoining the data on the moral equivalent of a "no-win no-fee" contract. Under these conditions, a lot of statistically-minded scientists will forget what they learned at college, and do what they are told, and torture the data. If the P-value is from one of a sequence of subset analyses, and is undertaken posterior to a main analysis which found nothing, then, arguably, the "prior odds" against an interesting difference might reasonably be worse than 100:1 against. Roger References Kirkwood BR, Sterne JAC. Essential medical statistics. Second edition. Oxford, UK: Blackwell Science; 2003. * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: logarithmic scales***From:*Roger Newson <roger.newson@kcl.ac.uk>

- Prev by Date:
**st: ml init: how to give a vector as starting values ?** - Next by Date:
**st: IVREG2 and AR(1) Correction** - Previous by thread:
**RE: st: logarithmic scales** - Next by thread:
**RE: st: logarithmic scales** - Index(es):

© Copyright 1996–2016 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |