What is shapiro test? How to perform it in R

Recipe Objective

What is shapiro test?

How to perform it in R? Shapiro test is a statistical test used to check whether the considered data is normally distributed data or not. In shapiro test, The null hypothesis is states that the population is normally distributed i.e if the p-value is greater than 0.05, then the null hypothesis is accepted. The alternative hypothesis states that the population is not normally distributed i.e if the p-value is less than or equal to 0.05, then the null hypothesis is rejected. This recipe demonstrates an example on shapiro test in R.

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Step 1 - Define a hypothesis

Null Hypothesis : The population is normally distributed. Alternate Hypothesis : The population is not normally distributed.

Step 2 - Define a data

set.seed(10) data_1 <- rnorm(50) set.seed(30) data_2 <- rnorm(50)

Step 3 - Use the shapiro-test

shapiro.test(data_1) # As the test returns a p-value less than 0.05, we reject the null hypothesis and conclude that the population data is not normally distributed.

"Output of code is :"

	Shapiro-Wilk normality test

data:  data_1
W = 0.97492, p-value = 0.3625

hist(data_1, col='steelblue') # the histogram shows that the curve is mildly left skewed in nature shapiro.test(data_2) # As the test returns a p-value greater than 0.05, we accept the null hypothesis and conclude that the population data is normally distributed.

"Output of code is :"

	Shapiro-Wilk normality test

data:  data_2
W = 0.9888, p-value = 0.9143

hist(data_2, col='steelblue') # the histogram shows that the curve is normally distributed in nature {"mode":"full","isActive":false}