# Normality Tester

Comprehensive normality testing using multiple statistical methods

> Canonical page: https://elysiatools.com/en/tools/normality-tester

- **Category:** Data Analysis

- **Keywords:** normality, statistical test, distribution, shapiro-wilk, anderson-darling

## Overview

The Normality Tester provides a comprehensive statistical analysis to determine if your dataset follows a normal distribution. By applying rigorous mathematical tests and visual diagnostics, this tool helps you validate assumptions required for parametric statistical modeling.

## Inputs

- **Data Input** (textarea): Enter numeric data separated by commas, spaces, or newlines Examples: 25, 27, 29, 30, 31, 32, 33, 34, 35, 36 Or: 25 27 29 30 31 32 33 34 35 36 Or: 25 27 29 30 31 32 33 34 35 36
- **Significance Level (α)** (select)
- **Include Visual Tests (Q-Q Plot)** (checkbox)

## When to use

- Before performing parametric tests like t-tests or ANOVA that assume normal distribution.
- When validating the quality of collected experimental or observational data.
- To check if residuals in a regression model are normally distributed.

## How it works

- Paste your numeric dataset into the input field, using commas, spaces, or newlines as separators.
- Select your desired significance level (α) to define the threshold for rejecting the null hypothesis.
- Enable the Q-Q plot option to visually inspect how well your data aligns with a theoretical normal distribution.
- Review the calculated test statistics and p-values to determine if your data significantly deviates from normality.

## Use cases

- Validating clinical trial measurements for parametric analysis.
- Checking financial return data for normality before risk modeling.
- Verifying process control measurements in manufacturing quality assurance.

## Frequently asked questions

### What does a p-value less than the significance level mean?

It indicates that you reject the null hypothesis, suggesting your data significantly deviates from a normal distribution.

### Which statistical methods are used?

The tool utilizes standard industry methods including Shapiro-Wilk and Anderson-Darling tests to assess distribution characteristics.

### Why should I use a Q-Q plot?

A Q-Q plot provides a visual confirmation of normality; if data points fall along a straight diagonal line, the data is likely normally distributed.

### What is the recommended significance level?

0.05 is the standard choice in most scientific research, representing a 95% confidence level.

### Is there a limit to the amount of data I can test?

The tool is designed for efficient processing of numeric lists; ensure your data is formatted as simple numbers for the best results.

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## Samples

- [Android Image Processing Java Samples](https://elysiatools.com/en/samples/android-image-processing-java): Android Java image processing examples including reading/saving images, scaling, and format conversion
- [Android Image Processing Kotlin Samples](https://elysiatools.com/en/samples/android-image-processing-kotlin): Android Kotlin image processing examples including reading/saving images, scaling, and format conversion
- [Web Image Processing Python Samples](https://elysiatools.com/en/samples/web-image-processing-python): Web Python image processing examples using PIL/Pillow including reading, saving, resizing, and format conversion
- [Web Image Processing Rust Samples](https://elysiatools.com/en/samples/web-image-processing-rust): Web Rust image processing examples including image read/save, scaling, and format conversion