Data normalization is the process that reduces variations of data
- Data may have some variations that are originated from each of measurements.
- The variations should be reduced as they will disturb precise data comparisons across measurements.
- Data normalization is the process that reduces the variations to make data comparable across measurements.
Unless the variations are canceled, precise comparisons cannot be drawn across measurement.
The common principle of normalization
Data normalization requires the following:.
- Identifying a definable character (= standard) among measurements
- Making the character to be uniform by a definite calculation method
The critical points of normalization
- The standard you choose can really be universally found among measurements.
- The calculation method is suitable to cancel the variations.
- The standard and calculation method are verifiable. Arbitrary ones should carefully be avoided.