# Data normalization choice [duplicate]

What are the main advantages and disadvantages of normalization between 0 and 1 or the other zero mean variance one algorithm? If we want to preprocess the data, how to select either of these two normalization methods?

• What is "the other zero mean variance algorithm"? – Christoph Hanck Mar 26 '15 at 9:09
• Terminology here as elsewhere can be a sheer nuisance. Normalizing as used variously in statistical science can mean (1) scaling by (value $-$ minimum) / (maximum $-$ minimum) (2) scaling by (value $-$ mean) / SD (often called standardization, itself a term with multiple other meanings) (3) occasional variants on (2) with e.g. median and IQR instead (4) transforming to approximate normality (Gaussianity) of distribution. I doubt that is a complete list; regardless, it is almost always advisable to give a formula too for what you discuss. – Nick Cox Mar 26 '15 at 11:39

• However, when you are dealing with heavy-tailed/skewed distributions, you can't rely on standardization ($\frac{x-\bar{x}}{\sigma_{x}}$), and maybe you'd prefer a unity-based normalization. Do you agree? – stochazesthai Mar 26 '15 at 12:46