First, any outlier that is significant on Grubbs' test, or any other test, on any variant of residuals, will, with N = 23, surely pass the IOTT. That's the interocular trauma test. It hits you between the eyes.

Second, in the old days (before powerful and ubiquitous computers) it was hard to do many kinds of regression. So, we tried to make the data fit the model. Today, we can model much larger data sets than yours with all sorts of models. If you suspect outliers (either a priori or by visual inspection) then you can do robust regression or quantile regression, or perhaps (depending on the nature of your variables) transform something.

The link EdM gave has a lot of good info on outliers in general and for regression.

EDIT: Given that this is "at the request of an editor" (per your edit) I would still not do it. Rather, I would cite the answers here (and in the link Ed put it).

I often do statistical editing for journal articles and I get to see that other editors (not trained in statistics, or only minimally trained) often make rather unfortunate requests. If you have information on who this editor is (i.e. are they statisticians?) you can pitch your argument differently - even statistical editors can make mistakes, but this sort of mistake is more likely from someone who took stats back in grad school and vaguely remembers "outliers bad".

First, any outlier that is significant on Grubbs' test, or any other test, on any variant of residuals, will, with N = 23, surely pass the IOTT. That's the interocular trauma test. It hits you between the eyes.

Second, in the old days (before powerful and ubiquitous computers) it was hard to do many kinds of regression. So, we tried to make the data fit the model. Today, we can model much larger data sets than yours with all sorts of models. If you suspect outliers (either a priori or by visual inspection) then you can do robust regression or quantile regression, or perhaps (depending on the nature of your variables) transform something.

The link EdM gave has a lot of good info on outliers in general and for regression.

EDIT: Given that this is "at the request of an editor" (per your edit) I would still not do it. Rather, I would cite the answers here (and in the link Ed put it).

I often do statistical editing for journal articles and I get to see that other editors (not trained in statistics, or only minimally trained) often make rather unfortunate requests. If you have information on who this editor is (i.e. are they a statistician?) you can pitch your argument differently - even statistical editors can make mistakes, but this sort of mistake is more likely from someone who took stats back in grad school and vaguely remembers "outliers bad".

First, any outlier that is significant on Grubbs' test, or any other test, on any variant of residuals, will, with N = 23, surely pass the IOTT. That's the interocular trauma test. It hits you between the eyes.

Second, in the old days (before powerful and ubiquitous computers) it was hard to do many kinds of regression. So, we tried to make the data fit the model. Today, we can model much larger data sets than yours with all sorts of models. If you suspect outliers (either a priori or by visual inspection) then you can do robust regression or quantile regression, or perhaps (depending on the nature of your variables) transform something.

The link EdM gave has a lot of good info on outliers in general and for regression.

First, any outlier that is significant on Grubbs' test, or any other test, on any variant of residuals, will, with N = 23, surely pass the IOTT. That's the interocular trauma test. It hits you between the eyes.

Second, in the old days (before powerful and ubiquitous computers) it was hard to do many kinds of regression. So, we tried to make the data fit the model. Today, we can model much larger data sets than yours with all sorts of models. If you suspect outliers (either a priori or by visual inspection) then you can do robust regression or quantile regression, or perhaps (depending on the nature of your variables) transform something.

The link EdM gave has a lot of good info on outliers in general and for regression.

EDIT: Given that this is "at the request of an editor" (per your edit) I would still not do it. Rather, I would cite the answers here (and in the link Ed put it).

I often do statistical editing for journal articles and I get to see that other editors (not trained in statistics, or only minimally trained) often make rather unfortunate requests. If you have information on who this editor is (i.e. are they statisticians?) you can pitch your argument differently - even statistical editors can make mistakes, but this sort of mistake is more likely from someone who took stats back in grad school and vaguely remembers "outliers bad".