That is, confounding could go both ways here; the effect could be greater than it appears, rather than less.
Absolutely, but if we assume the null hypothesis until proven otherwise, we will prefer to think of confounding as creating effect that is not there, rather than subduing an even stronger effect.
I’ll reanalyse that way and post results, if I remember.
Yes, please do! I suspect (60 % confident maybe?) the effect will still be at least a standard error, but it would be nice to know.
I made a script run in the background on my PC, something lik
Ah, bummer! I also have this problem solved for computer time, and I was hoping you had done something for smartphone carriage.
(Note, by the way, that a uniformly random delay is not as surprising as an exponentially distributed delay. Probably does not matter for your usecase, and you might already know all of that...)
Absolutely, but if we assume the null hypothesis until proven otherwise, we will prefer to think of confounding as creating effect that is not there, rather than subduing an even stronger effect.
Yes, please do! I suspect (60 % confident maybe?) the effect will still be at least a standard error, but it would be nice to know.
Ah, bummer! I also have this problem solved for computer time, and I was hoping you had done something for smartphone carriage.
(Note, by the way, that a uniformly random delay is not as surprising as an exponentially distributed delay. Probably does not matter for your usecase, and you might already know all of that...)
I added intention-to-treat statistics in an addendum.