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.
- 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?