When should the Z-score technique be used for data normalization?

The Z-score technique is best applied when the data is normally distributed, forming a proper bell-shaped curve. It aims to transform data into a distribution with mean 0 and standard deviation 1.

Conditions

The data is normally distributed.

Reasoning, step by step

Check if the data forms a symmetric bell-shaped curve.
If the data is normally distributed, apply the Z-score technique to standardize the data.

Example

The speaker draws a bell-shaped curve on the whiteboard and states that if the data is normally distributed, forming a proper curve or mirror image, the Z-score technique is best.

Common misconceptions

Assuming Z-score normalization is suitable for all types of data distributions regardless of shape.

Watch the explanation

YouTubeLec - 9 : Normalization in Data Transformation | Min-Max & Z-score Techniques with example

6:10 – 6:21Watch this moment ↗

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