Normalizing data for a feature in the Features table allows the user to specify that the values (and hence the distances) for this feature should be normalized. This is done by converting values to standard deviations. If all features are normalized, then each contributes equally to the total distance between artifacts (or assemblages). Otherwise, a feature whose values range from zero to a thousand would generally have a much greater contribution to distance than a feature that ranges from zero to ten. Normalization is particularly useful if different features are expressed in very different units (for example, weight and length, or tons and ounces) or if different distance functions (Euclidean, Manhattan or Hamming) are used for different features, particularly for features using Euclidean or Manhattan distance if another feature uses Hamming distance. The intention of normalizing data is to make one feature as comparable as possible to another as far as its contribution to the total distance between artifacts is concerned. It is probably not a very good idea to normalize features using Hamming distance.