  # Normalized Data / Normalization

Normal Distributions > Normalized Data / Normalization

The word “normalization” is used informally in statistics, and so the term normalized data can have multiple meanings. In most cases, when you normalize data you eliminate the units of measurement for data, enabling you to more easily compare data from different places. Some of the more common ways to normalize data include:

This list is by not means all-inclusive. I’ve included the most common ones, but be aware there are many, many other meanings for the word normalization.

## Normalization vs. Standardization

The terms normalization and standardization are sometimes used interchangeably, but they usually refer to different things. Normalization usually means to scale a variable to have a values between 0 and 1, while standardization transforms data to have a mean of zero and a standard deviation of 1. This standardization is called a z-score, and data points can be standardized with the following formula:

Where:

Z-scores are very common in statistics. They allow you to compare different sets of data and to find probabilities for sets of data using standardized tables (called z-tables). For more about z-scores, see: Z-score: Definition, Formula, and Calculation.

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Statistical concepts explained visually - Includes many concepts such as sample size, hypothesis tests, or logistic regression, explained by Stephanie Glen, founder of StatisticsHowTo.