Resolved Question How to deal with positively skewed data? I have an SPSS dataset with multiple variables. Some variables are moderately to severely positively skewed (high skewness & kurtosis). However, I expected such variables to be skewed because they capture constructs (e.g., paranoid thinking) that are not frequently found in the normal population. I also have outliers, ie. cases with extreme scores on the variables. I need to perform multiple regression analyses on the data, but the normality assumption underpinning regression is "violated" because my data is positively skewed. But I also have a large datafile with 313 cases. Some stats books I have consulted say that skewness & kurtosis influences disappear in large samples (200+ cases). Questions: 1. should I perform a logarithmic transformation? 2. if data transformation is necessary, do I transform ALL the variables, or just those that are skewed? Best Answer - Chosen by Voters It seems to me that you have two separate problems, the skew and the outliers, and I don't know if there is a standard prescription for either. I'd be inclined to omit the outliers from the analysis, just mention them in the results section, and try to make sense of them in the discussion. As for the skew, what you do about that depends on how skewed is the skew. Most of the common statistical tests are quite robust, so you might be able to get away with not transforming the data. Of course, a transformation doesn't interfere with reading p-values