This is a common yet one of the most important data visualization in R interview questions and answers for experienced professionals, don't miss this one. Response: We can use a q-q plot for this. Let us take an example. set.seed(123) # Normally distributed numbers x <- rnorm(100, mean=50, sd=5) # Uniformly distributed numbers z <- runif(100) We can compare the numbers sampled with rnorm() against normal distribution. qqnorm(x) qqline(x) We can then experiment with the same numbers to the 3rd power, compared to the normal distribution. qqnorm(x^3) qqline(x^3) Numbers sampled from the flat distribution, compared to normal is described below. qqnorm(z) qqline(z)