How reliable is a linear model on log-transformed data I have collected timing data in which the residuals are non-normally distributed. I log-transformed the data, and then conducted a linear mixed-model regression analysis. (The residuals from the log-transformed data are much "more" normal, but not normal.) The results show a significant difference between two conditions, which is what I was hoping for. However, if not log-transforming the data, the difference is no longer significant. In addition, if not log-transforming the data, but instead removing outliers (which then results in normally distributed data), the difference is also no longer significant.


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*IV's: time (raw) or log-transformed time

*DV's: categorial and numerical

*Random effects: Per person (6 times per person)


I’m hoping to get the stats community's opinion as I'm not a statistician. Would you rely on the log-transformation? Or would you rather use a non-parametric method? Or, third idea, use the linear regression on non-normally distributed data and later check residuals?
Residuals from raw data

Residuals from log-transformed data

 A: 1) You do not need the raw data to be normally distributed. It's only the residuals that need to be. 
2) Removing 'outliers' is generally a bad idea unless you have very good reason to believe that those data points are invalid for some reason, such as instrument failure. 
3) If the residual distribution is actually a problem, you can still avoid log transformation by changing the assumed distribution from Gaussian to something else using a generalized linear mixed model. Transformation might be fine, though.
4) To address the question in your title, which is a bit different from the text of your question: the validity of the data has nothing to do with whether it is transformed or not.  [Removed after title changed]
A: You ask:

Would you rely on the log-transformation? Or would you rather use a
  non-parametric method?

for me, that would depend on what the variables are and what my question is. Does taking the log make substantive sense? It often does make sense for variables involving money (such as income, wealth, expenditures) because we tend to think of those variables multiplicatively rather that additively - that is, the difference between a salary \$20,000 and \$25,000 is much larger than between \$200,000 and \$205,000. So, if log transform makes sense, do that.
If log transform doesn't make sense, then I'd try a method that doesn't assume normal residuals - e.g. quantile regression. 
