To be explicit about when the offset is being added, I'm suggesting replacing your log1p(x) ≣ log(1 + x) transformation with log(c + x) for c=10 or c=100.
Which will do what, exactly? What does this accomplish? If you think it does something, please explain more clearly, preferably with references explaining why +10 or +100 would make any difference, or even better, make use of the full data which I have provided you and the analysis code, which I also provided you, exactly so criticisms could go beyond vague speculation and produce something firmer.
(If I sound annoyed, it's because I spend hours cleaning up my analyses to provide full source code, all the data, and make sure all results can be derived from the source code, to deal with this sort of one-liner objection. If I didn't care, I would just post some coefficients and a graph, and save myself a hell of a lot of time.)
Here's why it matters:
If we add 100 to everything, that transformation will be sized differently after we take the log. 0s go from -infinity to +2, a jump of infinity (...plus 2, to the degree that makes any sense); 100s go from 2 to 2.3, a jump of .3. If we added 1 instead, 0s would go from infinity to 0, and 100s would go from 2 to 2.004. If we added .01, 0s would go to -2, and 100s would go to 2.00004.
But what does that do to our trendline? Suppose that 40% of EAs gave 0, and 60% of non-EAs gave 0. Then I when I calculate the mean difference in log-scal...
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