---
title: normality
description: Learn about normality statistics and their significance in data analysis.
  Discover different techniques such as kurtosis, skewness, Jarque-Bera, Shapiro-Wilk,
  and Kolmogorov-Smirnov for evaluating normality in time series data. Explore how
  these tests can help determine if a data sample follows a normal distribution.
keywords:
- normality statistics
- kurtosis
- skewness
- Jarque-Bera
- Shapiro-Wilk
- Kolmogorov-Smirnov
- time series data
- target column
- normality tests
---


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import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';

Get Normality Statistics.

- **Kurtosis**: whether the kurtosis of a sample differs from the normal distribution.
- **Skewness**: whether the skewness of a sample differs from the normal distribution.
- **Jarque-Bera**: whether the sample data has the skewness and kurtosis matching a normal distribution.
- **Shapiro-Wilk**: whether a random sample comes from a normal distribution.
- **Kolmogorov-Smirnov**: whether two underlying one-dimensional probability distributions differ.

Parameters
----------
data : List[Data]
Time series data.
target : str
Target column name.

Returns
-------
OBBject[NormalityModel]
Normality tests summary. See qa_models.NormalityModel for details.

```python wordwrap

```

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## Parameters

This function does not take standardized parameters.

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## Returns

This function does not return a standardized model

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