1.1 Test for significance: Correlation coefficient

After calculating the sample correlation coefficient \(r\), we can take it one step further and test for the significance of the correlation coefficient. That is, we can test the null hypothesis that the population correlation coefficient is 0, using

\[H_0:\rho = 0 \;\;\text{versus}\;\;H_1: \rho \neq 0,\]

where:

  • \(\rho\) denotes the true (population) correlation coefficient.

By carrying out this hypothesis test, we can determine whether or not the correlation coefficient is significant. In other words, we reject \(H_0\), we conclude there is evidence to suggest that the correlation is not equal to zero. This would mean we have evidence that there is a significant linear relationship (or association) between the two variables. On the other hand, if we do not reject \(H_0\), this means we do not have enough evidence to conclude that the correlation is not equal to zero. If the correlation is equal to zero, this means there is no linear relationship between the two variables.

Let's carry out the test to determine whether there is evidence of a significant association between the income and happiness variables:


    Pearson's product-moment correlation

data:  df$income_2019 and df$happiness_2019
t = 10.278, df = 76, p-value = 4.945e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.6504941 0.8422277
sample estimates:
      cor 
0.7626303

From the above output, we note the following:

  • The \(p\)-value is almost 0, which is much less than 0.05, so we reject \(H_0\). That is, there is evidence to suggest that the population correlation coefficient is not equal to zero. This means we have evidence of a significant association between the two variables
  • The test statistic is \(t = 10.278\)
  • The sample correlation coefficient (i.e. the estimated correlation) is \(r = 0.76\)
  • The 95% confidence interval for the population correlation coefficient \(\rho\) is (0.65, 0.84). That is, we are 95% confident that the true correlation between income and happiness is between 0.65 and 0.84.
These notes have been prepared by Amanda J. Shaker. The copyright for the material in these notes resides with the author named above, with the Department of Mathematics and Statistics and with La Trobe University. Copyright in this work is vested in La Trobe University including all La Trobe University branding and naming. Unless otherwise stated, material within this work is licensed under a Creative Commons Attribution-Non Commercial-Non Derivatives License CC BY-NC-ND