Statistical normality, also known as Gaussian distribution, is a statistical property that describes how a set of data is distributed. Normal distribution is important because it allows researchers to make inferences about the population from which the data was drawn.
Checking for normality is an important step in many statistical analyses. There are a number of different ways to check for normality, including:
How to Check for Normality is a statistical procedure used to determine whether a given dataset conforms to a normal distribution, also known as a Gaussian distribution. Normality is a crucial assumption in many statistical analyses, and checking for normality helps ensure the validity of the results.
There are several reasons why checking for normality is important. First, many statistical tests, such as t-tests and ANOVA, assume that the data being analyzed is normally distributed. If the data is not normally distributed, the results of these tests may be inaccurate or misleading. Second, normality is often a requirement for using certain statistical models, such as linear regression and logistic regression. If the data is not normally distributed, these models may not be able to accurately predict the relationship between variables.
