  While the utilisation of different methods of outliers correction has been
shown to counteract the inferential error produced by the presence of
contaminating data not belonging to the studied population; the effects
produced by their utilisation when samples do not contain contaminating
outliers are less clear. Here a simulation approach shows that the most popular
methods of outliers correction (2 Sigma, 3 Sigma, MAD, IQR, Grubbs and
winsorizing) worsen the inferential evaluation of the studied population in
this condition, in particular producing an inflation of Type I error and
increasing the error committed in estimating the population mean and STD. We
show that those methods that have the highest efficacy in counteract the
inflation of Type I and Type II errors in the presence of contaminating
outliers also produce the stronger increase of false positive results in their
absence, suggesting that the systematic utilisation of methods for outliers
correction risk to produce more harmful than beneficial effect on statistical
inference. We finally propose that the safest way to deal with the presence of
outliers for statistical comparisons is the utilisation of non-parametric tests
