Talk:Look-elsewhere effect

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Above undated message substituted from Template:Dashboard.wikiedu.org assignment by PrimeBOT (talk) 00:15, 18 January 2022 (UTC)

particular to particle physics?
The first sentence claims that this effect is particular to particle physics. Can we support that properly? As far as I know this effect (although not always under this name) is frequently brought up in discussion around various epidemiological studies. These experiments typical measure a large number of parameters of the studied population. If you then simply calculate all possible correlations between the parameters. The odds are that one of them turns up as significant.TR 16:00, 16 December 2011 (UTC)
 * The existing phrasing suggests specially but not exclusively, so you could broaden by adding mention of that or other realms, but since as noted the first four cites used physics examples (actually first seven I think), it certainly occurs and gets discussed in that sphere. I pulled the expert tag since for that reason it appears unnecessary.  Chris Rodgers (talk) 06:58, 20 April 2015 (UTC)

This is a phenomenon of statistics
This is a phenomenon that arises in the study of any complex system where the signal to noise ratio is low (ie when the data are noisy). And of course this includes medical/biological systems as well as many others including many (but not all) systems studied by Physicists. The phenomenon is a direct result of the analyses normally prescribed by Statistics and the assumptions upon which the design of these analyses are based.

Among biological/medical scientists the phenomenon is referred to as Type I error. Physicists don't speak about the phenomenon in the same way as biological/medical scientists because the signal to noise ratio in most Physics experiments is much higher and so this type of phenomenon is far less common and much more remarkable (where remarkable is defined as 'likely to raise eyebrows and/or drop jaws')

This really doesn't deserve a Wikipedia entry. It should be mentioned as a paragraph in the Wikipedia page on Type I Error. — Preceding unsigned comment added by 24.212.226.188 (talk) 01:53, 18 January 2013 (UTC)
 * I'd have to dissent; you could as easily argue it's the same phenomenon as the law of large numbers. There's a fuzzy gray area, but this describes more the behavioral angle specifically.  I think it has validity both as a commonplace descriptor and as a specific subflavor of error.  Chris Rodgers (talk) 06:48, 20 April 2015 (UTC)

"Texas sharpshooter fallacy" vs. "Look-elsewehere effect"
The above articles overlap. I suggest they be merged or the differences be emphasized. EasternGrace (talk) 08:08, 24 June 2014 (UTC)

Multiple comparisons problem
- This page seems like a redundant and less fleshed out version of the "Multiple comparisons problem" article that already exists on Wiki - First sentence mentions "particle physics experiments" as being susceptible to the look-elsewhere effect, seems sort of random and there are plenty of other fields where the look-elsewhere effect occurs — Preceding unsigned comment added by Oliviaxu17 (talk • contribs) 18:10, 7 October 2016 (UTC)

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Subsequent studies on Power Lines
The article claims "Subsequent studies failed to show any links between power lines and childhood leukemia". This is not the case. The largest study to date is the Draper et al (2005) study published in the British Medical Journal, doi:10.1136/bmj.330.7503.1290, which again found excess childhood leukemia out to even 600 m from High Voltage lines. I do not believe in the claim of the study, but I do think the statement in this wikipedia article should be modified to acknowledge this result. — Preceding unsigned comment added by 134.124.123.83 (talk) 15:25, 13 April 2021 (UTC)

Trying to fix misuse of p-value in this page itself
This article, in the first sentence of the section Use, stated that: "Many statistical tests deliver a p-value, the probability that a given result could be obtained, assuming random coincidence." This is not accurate, and I tried to improve it. I worry a bit that the new formulation could be confusing to a layman, however. If anyone would like to improve it that would be appreciated! For reference, the page Misuse of p-values states the following: "The p-value is not the probability that the observed effects were produced by random chance alone. The p-value is computed under the assumption that a certain model, usually the null hypothesis, is true. This means that the p-value is a statement about the relation of the data to that hypothesis."