# Talk:2560: Confounding Variables

This is the earliest in the day that I can recall a comic being published in a long time. It's not even noon ET yet. Barmar (talk) 16:39, 27 December 2021 (UTC)

This explanation will not be complete until someone explains the title text. I assume controlling for the residual will lead to a null result, but I don't know statistics well enough to know. 172.70.110.171 17:10, 27 December 2021 (UTC)

"Stats are a farce and the truth is unknowable." She jests, but... 172.70.178.25 18:31, 27 December 2021 (UTC)

Many people have made similar observations over the centuries [1] Seebert (talk) 16:31, 28 December 2021 (UTC)
Stats provide answers, but not questions - that's what my professor used to say 141.101.104.14 12:04, 30 December 2021 (UTC)

"(Why was the word "remit" used and why was it linked to Mythbusters? Both were unnecessarily confusing.)"... Well, "remit" ≈ "purpose" as I used it (Wiktionary, Noun section, item 1: "(chiefly Britain) Terms of reference; set of responsibilities; scope. quotations" - seeing that it's apparently British, and the remit scope of most of the verb forms, I now understand your confusion). And the suggestion was that this was not a (bad/offputting?) Statistics course but a course that "mythbusted" a range of subjects, somewhat like Every Major's Terrible, that we catch just as Statistics in particular gets a page or so of short shrift about. 172.70.85.73 23:01, 29 December 2021 (UTC)

Her argument is exceedingly weak. If you simply want to note a correlation, there's no need to control for any confounding variables. If, on the other hand, you want to prove a causative relationship, there is simply no way to do that by controlling for any set of potentially confounding variables - but contrary to her assertion, this doesn't mean that stats are a farce and the truth is unknowable, it just means that a causative relationship can only be established experimentally. To establish a causative relationship, you don't need to control for any confounding variables, you just randomize. Example in a clinical trial, patients are randomly assigned to the treatment group or the placebo group. There's no need to control for anything because random chance would distribute patients with any potential confounding variable throughout both groups. You use appropriate statistical significance tests to make sure that this effect of random chance is sufficient to be reasonably sure of the results, and you have the truth. The argument this individual is making is based on misunderstandings of statistical principles prevalent in popular culture. 172.68.174.150 18:46, 30 August 2023 (UTC)

Her assertion is bad, but that's the humour of the comic.
Meanwhile, a clinical trial 'deconfounds' by careful balancing of those accepted into the study (and enough in the cohorts to make randomised active/control/etc splitting unlikely to create unbalance in comparison), enough to make it not significantly possible that chance differences in each group can explain any effect seen.
But a big problem (not really dealt with above) has been that confounding variables have been squashed by, e.g., not enrolling women to a study because of the complication of their monthly biological cycles interacting with the treatment... leaving the study biased by only being analysed in the context of male endochronic systems.
Then there's also the tendency for Phase III ('first in human') to have ended up with overwhelmingly young, male and (often, given the catchment advertised to) white volunteers, which colours how it progresses onwards into the theraputic use phases (for which the demographic should be more self-balancing to than merely looking for students who are cash-poor but relatively time rich, in the worst cases), but this will all vary with how much past pitfalls have been identified and learnt from.
Of course, the tutor here isn't looking at purely phramcological stats (that's not even hinted at), and the careful split of (preaccumulated) data into training and testing sets is perhaps the best way to emulate the same A/B or A/control testing regimes when you aren't able to allocate a different 'treatment' in advance of the data collection. For which you maybe need to have twice as many datapoints as you might if you were happy to just effect-fish within the entire original dataset.
...but stats is complicated. Easy to overuse ("lies, damn lies and statistics"), easy to dismiss (as parodied here). A mere few paragraphs of explanation is always going to be lacking in nuance (I'd prefer to add much more detail to this reply, ideally, but it'd probably be unreadable), and the portion of comic-monologue we see is deliberately skimming over all the issues.
Plus we don't get to see the supplementary materials, or exactly what her diagrams are, nor anything of whatever lecture led up to this short summary, e.g. in what context she speaks (statistical theology?) Which doesn't really matter, in the context of the joke, and wouldn't help it anyway. 172.71.242.77 21:04, 30 August 2023 (UTC)