# Z-Score Normalization

## Z-Score Normalization – (Data Mining)

Z-Score helps in the normalization of data. If we normalize the data into a simpler form with the help of z score normalization, then it’s very easy to understand by our brains.

## How to calculate Z-Score of the following data?

 marks 8 10 15 20

Mean = 13.25

Standard deviation = 4.6

 marks marks after z-score normalization 8 -1.14 10 -0.7 15 0.3 20 1.4

## How do you use a z score table?

1. We can find a specific area under the normal distribution curve.
2. We can find the z-score of the data value and use a Z-Score Table.
Z-Score Table is used to find the area.
A Z-Score Table shows the area percentage to the left of a given z-score on a standard normal distribution.

## Advantages of the z score

The z-score is very useful when we are understanding the data. Some of the useful facts are mentioned below;
The z-score is a very useful statistic of the data due to the following facts;
It allows a data administrator to understand the probability of a score occurring within the normal distribution of the data.

The z-score enables a data administrator to compare two different scores that are from different normal distributions of the data.

## Is a higher or lower Z score better?

Suppose we have data from two persons. Person A has a high Z score value and person B has a low Z Score value. In this case, the higher Z-score indicates that Person A is far away from person B.

## What does a negative and a positive z score mean?

A negative z-score indicates that the data point is below the mean.
A positive z-score indicates that the data point is above the mean.

## Why is the mean of Z scores is 0?

The standard deviation of the z-scores is always 1 and similarly, the mean of the z-scores is always 1.
Z-scores values above 0 represent that sample values are above the mean.
z-scores values below 0 represent that sample values are below the mean.
In the case of squared z-scores, the sum of the squared z-scores is always equal to the number of z-score values.

## What is the meaning of the high Z score and low Z score?

• Suppose we have a high Z-score value then it means a very low probability of data above this Z-score.
• Suppose we have a low Z-score value then it means a very low probability of data below this Z-score.