Data standardization is a statistical method that is used to transform data so that it has a mean of zero and a standard deviation of one. This is often done to make the data more comparable or to simplify the analysis.
There are several ways to standardize data, but the most common method is to subtract the mean from each data point and then divide by the standard deviation. This results in a new set of values with a mean of zero and a standard deviation of one.
Standardization is useful when comparing data from different sources or when the data has different units of measurement. For example, if you want to compare the heights of people in two different countries, you could standardize the data by converting the heights to standard deviation units (also known as z-scores). This would allow you to compare the data on a common scale, regardless of the units of measurement used in the original data.
It's important to note that standardization does not affect the shape of the data distribution, only the location and scale. Therefore, standardization should not be used to normalize data that is not normally distributed. In these cases, a data transformation may be more appropriate.