# Am I Log Normalizing correctly?

I have searched the other questions about log normalization and all assume a level of understanding that I don't have.

I have some ecological data and I am told that it needs to be log transformed. My dependent variables are relative densities of compounds found in the plants, and the independent variables have to do with location (A or B), distance from a trail (on a trail or not on a trail), and a number of covariants like pH and soil moisture. I have done ANCOVA's with the two independent variables adding the different covariants. However, I apparently need to log normalize the data

As I understand it, I need to take either the base ten or base e log of my variables. I can do that fine for the relative densities as they are numerical, but what do I do when my independent variables are categorical like location A v. B? Also, what do I do with the covariants, all of which are numerical?

• Could you tell us on what basis you need to "log normalize the data"? Certainly you don't need to take logarithms of all the numbers in sight! – whuber Apr 9 at 18:05

ANOVA, MANOVA and ANCOVA assume normally distributed residuals of the data. They are parametric methods, so before performing them, you should check whether your data matched the assumptions made.

So what log-tranforming does is it stabilizes log-normally distributed data. If your data has a distribution like this and is positively skewed, then log-transforming the data makes it more normally distributed. Then it's better suited for analysis of this kind.

Now you can think again on which columns you should perform this. Explore the characteristics of your data before diving head in.

• ctd ... - firstly note that "parametric" and "normal" are not synonyms (a normal model is merely one example of a parametric assumption). Secondly, in some situations a person may very reasonably use a nonparametric procedure even when normality is a perfectly adequate assumption; or may well use a parametric procedure -- just not necessarily one that assumes normality -- when normality is not an adequate assumption. Finally, one might look to robust methods, for example. $\:$ The rest of the answer looks okay (I have some minor quibbles) – Glen_b -Reinstate Monica Apr 10 at 0:10