I Is there a term for this type of unknown experimental interference?

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The discussion centers on the importance of identifying potential confounding variables when interpreting experimental data. Even with a significant correlation between input A and output C, the presence of an unknown variable B could invalidate the results. The timing of data collection, environmental factors, and experimenter effects are highlighted as possible confounders. Researchers must ensure that their experiments are designed to minimize these unknown influences before publishing results. The term "confounders" is suggested as a suitable label for these unknown alternate causes.
.Scott
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TL;DR Summary
Term for unknown alternate causes in experiments.
You collect 200 data points, 100 with input ##A## and 100 with input ##A'##.
For all 100 ##A## you get output ##C## and for all 100 ##A'## you get ##C'##.
That's way more than 5 standard deviations, so you're ready to publish...

But not really.

Because what matters is not just that input ##A## is well-correlated to ##C## but that there is no ##B## that is also correlated to ##C##.
In the simplest (and probably worse) case, we may have collected all of the results from ##A## before any of the results from ##A'## - so there was a particular time when ##C## outputs ended and ##C'## outputs started. That would create many potential ##B##s: Room temperature or humidity were rising or falling; the experimenter was getting tired or more practiced; the apparatus broke or was wearing out.

So you are not ready to tout your 5 standard deviations until you run the experiment in a manner that reduces the likelihood that there could be such an unknown alternate cause. Is there a common (or otherwise better) term for such "unknown alternate causes"?
 
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Maybe "confounders"
 
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Likes .Scott
Dale said:
Maybe "confounders"
Yes, excellent! And I had run into that term before.
Thanks!
 
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