The table below shows the Pearson correlation critical values for different significance levels and degrees of freedom. Note that degrees of freedom = n-2 where n = # pairs of data.
Pearson Correlation Critical Values Table
Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike. My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.
Wow this is really helpful and accurate. I love it’s.
But one thing is not good for those who don’t know how to use their own brian for calculations this is because in the absence of network they can’t work on their own
I’m not sure if I understand this correctly. Is it now possible to accept/reject the null hypothesis just by comparing the calculated value of the Pearson r with the critical value of r from the above table? That is, if the calculated r > critical value of r, we reject the null hypothesis?
We know that the calculated value of r can also be negative. If so, every time we get a negative r, it will always result in the acceptance of the null hypothesis because the critical r from the table are all positive and a negative r is always lesser than the critical r. Therefore, a negative correlation will always be ‘not significant’, even if it is close to -1.0 in value.
Hi Manuel, I’m currently taking an intro stats class and my teacher has discussed this topic recently. When you’re comparing r values to critical values, if you have a negative r value then its corresponding critical values are negative.
To illustrate this, if you have a size 5 sample population with 1-tailed 0.025 significance level (critical value: 0.754), then r = -0.8 would be statistically significant as |r|<0.754 is true. This is still assuming that the association is linear.
So a negative correlation does not necessarily mean you automatically accept the null hypothesis.