The logarithmic and square root transformations are typically most effective at normalizing data that have a minimum value near 1 and have a range that is at most a few orders of magnitude. If a variable consists entirely of large positive values, the transformed data do not show improved normality.

For example, if the minimum value of your data is , you might want to subtract from your data as a first step so that the new minimum value is 1. You can translate (and scale) data by using the **a+b*Y** transformation in the **Scaling/Translation** family. Alternatively, the square root and logarithmic transformations are defined as **log(Y+a)** and **sqrt(Y+a)**, so you can specify negative values for the parameter in these transformations. An example of this is presented in the next section.