Caveat: This question may be a tad rambly, and I welcome comments with specific directions for me to improve it.
During a too brief exchange with the worthy @NickCox I got to thinking about transformation/back transformation and inference.
It seems to me pretty apparent that frequentist inference—confidence intervals, hypothesis tests—about a transformed variable $f(x)$, is not inference about the untransformed variable $x$, even when back-transforming inferential quantities (via $f^{-1}(x)$), because, generally, $\sigma^{2}_{f(x)} \ne f(\sigma^{2}_{x})$, unless $f(x) = x$, and both CIs and hypothesis tests rely upon an estimate of the variance. To quote from my answer here:
Basing CIs on transformed variables + back-transformation produces intervals without the nominal coverage probabilities, so back-transformed confidence about an estimate based on $f(x)$ is not confidence on an estimate based on $x$.
Likewise, inferences about untransformed variables based on hypothesis tests on transformed variables means that any of the following can be true, for example, when making inferences about $x$ based on some grouping variable $y$:
$x$ differs significantly across $y$, but $f(x)$ does not differ significantly across $y$.
$x$ differs significantly across $y$, and $f(x)$ differs significantly across $y$.
$x$ does not differ significantly across $y$, and $f(x)$ does not differ significantly across $y$.
$x$ does not differ significantly across $y$, but $f(x)$ differs significantly across $y$.
It is also very easy to imagine examples setting this point down sharply. For example, if $y_{i} = x_{i}$ then Pearson's $r=1.0$ for $y$ and $x$, but Pearson 's $r=0.0$ for $y$ and $x^{2}$ if the distribution and range of $x$ is symmetric about 0.
On the other hand, tricks like Oehlert's Delta method can provide a 'back-transformation' that approximates the correct variance of $x$ as an alternative to simply calculating it directly, or calculating it as $f^{-1}\left(\sigma^{2}_{f(x)}\right)$.
Good Nick Cox however, points out that to "estimate on a link scale and report on the original scale is central to generalized linear models," and that (if I understood correctly) inference on the geometric mean entails such back-transformation in the form $exp\left(\frac{\sum \log (x)}{n}\right)$.
When is it Ok to base inferences about (untransformed) $\boldsymbol{x}$ on back-transformations of estimates and inferences on $\boldsymbol{f(x)}$, and when is it not?
Second caveat: I am not calling Nick Cox out to defend any position with this question, and am genuinely interested in understanding when performing inference on $\boldsymbol{f(x)}$ but drawing conclusions about $\boldsymbol{x}$ based on back-transformation makes sense and does not make sense.