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Jul 20, 2022 at 13:33 comment added usul Have you seen this xkcd comic? "Significant" xkcd.com/882
Jul 20, 2022 at 13:22 history became hot network question
Jul 20, 2022 at 12:00 history tweeted twitter.com/StackStats/status/1549725833815887875
Jul 20, 2022 at 9:47 answer added Björn timeline score: 8
Jul 20, 2022 at 8:03 answer added Łukasz Deryło timeline score: 5
Jul 20, 2022 at 6:28 answer added Stefan timeline score: 8
Jul 20, 2022 at 6:11 comment added AdamO Multiple regression is not well defined, but (IMO) is usually taken to mean one regression model with multiple adjustment variables. However, analyses somewhat like what you depict above are routinely done: usually you see some of X4 or X5 added or dropped or combined or... anyway, the goal might be modeling the x1 association with y. By fitting many models, you increase the risk that one is spuriously identified as significant, and you report the overall x1 and y association as positive. In that case the familywise error rate needs to be controlled to preserve the true alpha level.
Jul 20, 2022 at 5:57 comment added mkt Thanks, that helps to understand this. Your question seems answerable but in the example you use, there are likely better ways to model the data (you could collapse all of these into one regression, for example). If the example is only intended to illustrate your point about multiple comparisons, that can probably be ignored.
Jul 20, 2022 at 5:45 comment added stats_noob @ mkt: for your second comment - yes, that is correct!
Jul 20, 2022 at 5:43 history edited stats_noob CC BY-SA 4.0
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Jul 20, 2022 at 5:42 comment added mkt Specifically, does each id correspond to a different individual, and does each y value in the same row represent a different condition/outcome for the same individual?
Jul 20, 2022 at 5:40 comment added mkt What precisely do each of the x and y values represent, in your data?
Jul 20, 2022 at 5:38 history edited stats_noob CC BY-SA 4.0
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Jul 20, 2022 at 5:13 history asked stats_noob CC BY-SA 4.0