Z-Score Normalization is a data transformation technique used to standardize the distribution of values in a dataset by setting the mean to 0 and the standard deviation to 1. This can be achieved through the following formula:
New Value = (x – μ) / σ
Where x is the original value
μ is the mean of the dataset
σ is the standard deviation of the data.
How is Z-score Normalization Useful?
- Z-score normalization can be employed to standardize financial data, such as stock prices, returns, and volatility, to assist analysts in making more informed investment decisions.
- In medical research, this technique is useful for standardizing measurements, like blood pressure, cholesterol levels, and BMI to facilitate comparisons between study populations and reveal underlying patterns and trends.
- Manufacturers use it to standardize production data like product weight, dimensions, and color, enabling them to pinpoint and correct errors, thus ensuring consistent quality.
- Social science researchers can use this standardization method for survey responses, personality traits, cognitive abilities, and similar data sets to identify patterns and relationships between study populations.
- Sports analysts also benefit from this technique by using it to standardize performance data such as player statistics, team rankings, and game outcomes. This allows analysts to compare data over different seasons and uncover key factors linked to success.
