Log transform


TS Contributor
Hardly ever, in my opinion. The purpose is to minimize the effects of violations of underlying assumptions, such as normality and homogeneity of variance, but procedures such as ANOVA and t-tests are pretty robust if only one of those assumptions is violated - and the violation needs to be really severe.

The other problem is that, once you've transformed the data, the practical interpretation of the results becomes difficult - explaining what a "log" or "natural log" is to a non-stats person is not going to go over well.
the only time i would do it is if i were trying to establish that the data are related by a log or power function. you can test this idea by tranforming the data to log and doing a linear regression if log regession is not availible. been a long time since i've done that though....

you might also do it to allow yourself the opportunity to plot a wide range of data on a single plot, John's caveat applies here too.