A brief review of 50 studies from the last 10 years indicated that it is often accepted practice to apply log transformation processes to raw EEG data. This practice is based upon the assumptions that (a) EEG data do not resemble a normal distribution, (b) applying a transformation will produce an acceptably normal distribution, (c) the logarithmic transformation is the most valid form of transformation for these data, and (d) the statistical procedures intended to be used are not robust to non-normality. To test those assumptions, EEG data from 100 community participants were analysed for their normality by reference to their skewness and kurtosis, the Kolmogorov–Smirnov and Shapiro–Wilk statistics, and shapes of histograms. Where non-normality was observed, several transformations were applied, and the data again tested for normality to identify the most appropriate method. To test the effects of normalisation from all these processes, Pearson and Spearman correlations between the raw and normalised EEG alpha asymmetry data and depression were calculated to detect any variation in the significance of the resultant statistic.