Friday, June 25, 2021

(R) The Levene's Test

In today’s article we will be discussing a technique which is not specifically interesting or pragmatically applicable. Still, for the sake of true data science proficiency, today we will be discussing, THE LEVENE'S TEST!

The Levene's Test is utilized to compare the variances of two separate data sets.

So naturally, our hypothesis would be:

Null Hypothesis: The variance measurements of the two data sets do not significantly differ.

Alternative Hypothesis: The variance measurements of the two data sets do significantly different.

The Levene's Test Example:

# The leveneTest() Function is included within the “car” package #


N1 <- c(70, 74, 76, 72, 75, 74, 71, 71)

N2 <- c(74, 75, 73, 76, 74, 77, 78, 75)

N_LEV <- c(N1, N2)

group <- as.factor(c(rep(1, length(N1)), rep(2, length(N2))))

leveneTest(N_LEV, group)

# The above code is a modification of code provided by StackExchange user: ocram. #

# Source #

This produces the output:

Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 1 1.7677 0.2049

Since the p-value of the output exceeds .05, we will not reject the null hypothesis (alpha = .05).


The Levene’s Test for Equality of Variances did not indicate a significant differentiation in the variance measurement of Sample N1, as compared to the variance measurement of Sample N2, F(1,14) = 1.78, p= .21.

So, what is the overall purpose of this test? Meaning, when would its application be appropriate? The Levene’s Test is typically utilized as a pre-test prior to the application of the standard T-Test. However, it is uncommon to structure a research experiment in this manner.  Therefore, the Levene’s Test is more so something which is witnessed within the classroom, and not within the field.

Still, if you find yourself in circumstances in which this test is requested, know that it is often required to determine whether a standard T-Test is applicable. If variances are found to be un-equal, a Welch’s T-Test is typically preferred as an alternative to the standard T-Test.


I promise that my next article will be more exciting.

Until next time.


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