Z-Score Normalization — standardisation to zero mean and unit standard deviation
Z-SCORE-NORMALIZATION (Z-Score Normalization — standardisation to zero mean and unit standard deviation) is a normalization multi-criteria decision-making (MCDM) method introduced by Hellwig, Z. in 1968. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
Read the full method
Sign in with a free account to read this section.
Method map
The neighbourhood of related methods — select a node to explore.
When to use it
z_ij ∈ (−∞,+∞); columns have mean=0 and population σ=1. Handles negative values and large-scale differences. The cost flip ensures higher z = better. Used in HELLWIG method and TAXONOMY.
Strengths & limitations
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
- Results depend on the chosen normalisation, weights, and parameter settings.
Sources
- Hellwig, Z. (1968). Zastosowanie metody taksonomicznej do typologicznego podzialu krajow ze wzgledu na poziom ich rozwoju oraz zasoby i strukture wykwalifikowanych kadr technicznych. Przeglad Statystyczny link ↗
How to cite this page
ScholarGate. (2026, June 2). Z-Score Normalization — standardisation to zero mean and unit standard deviation. ScholarGate. https://scholargate.app/en/decision-making/z-score-normalization
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
Compare side by side →