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We regularly get questions about log transforming dependent variables, which in some quarters appears to be seen as a magic silver bullet that solves all problems, because apparently, "everything is normally distributed after log-transforming" (which, to be explicit, is wrong, and not even useful). Here are a few useful prior threads in this vein (contributed by User1865345 and mkt):

As in this analogous question on imbalanced data, it probably does not make sense to attempt to merge them into the One Question To Rule Them All As A Canonical Duplicate. However, I would propose that we collect a couple of useful threads on this topic here (threads tagged both "data-transformation" and "logarithm" might be a good start) to use as duplicate targets, depending on the nuance of the newly posted question, and ask the moderators to slap the tag on them.

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  • $\begingroup$ There can be different types of issues with respect to log transforms of predictors versus outcomes. For predictors, there are things like dealing with 0 values and interpreting coefficients of transformed predictors. For outcomes, the OP has often jumped onto a log transformation without considering options like other variance-stabilizing transformations, Box-Cox, Gaussian GLM with log link, proportional-odds models, or nonparametric tests. Unfortunately, many threads cover both predictors and outcomes, making it harder for a new questioner to find just what's needed in a particular context. $\endgroup$
    – EdM
    Commented Oct 8, 2022 at 16:23

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