Shapiro-Wilk Normality Test | shapiro.test in R

Are you confident in your data analysis? 

Shapiro-Wilk test in R is essential to ensuring your data fits a normal distribution, but how well do you understand its mechanisms and implications? Can you enhance the reliability of your research findings and uncover deeper insights by learning this test? Start learning the complexities of this crucial statistical tool and challenge your understanding of data analysis.

The Shapiro-Wilk test is a widely utilized statistical method for assessing the normality of data distributions. It is used in various fields due to its robustness and effectiveness, especially when dealing with small sample sizes. The test was developed by Samuel Shapiro and Martin Wilk in 1965 and has since become a standard tool in statistical analysis. 

The primary function of the Shapiro-Wilk test is to evaluate the null hypothesis that a given sample is drawn from a normally distributed population. This is crucial in many statistical analyses, as many parametric tests assume the normality of the data. In practical applications, the Shapiro-Wilk test is often employed in studies that require validation of the normality assumption before conducting further statistical analyses.

# Perform the Shapiro-Wilk test
shapiro.test(mtcars_data$mpg)