Why are two random variables independent if the Pearson's correlation coefficient equals zero, but the same result does not hold for covariance?

I was reading the following book

Han J, Pei J, Kamber M. Data mining: concepts and techniques. Elsevier; 2011 Jun 9. (Third Edition)

On page 96, at the first line of the last paragraph it says (here)

If the resulting value is equal to $0$, then $A$ and $B$ are independent and there is no correlation between them.

where the resulting value above corresponds to the following formula (correlation coefficient)

$$r_{A,B}=\frac{\sum_{i=1}^n (a_i - \overline{A}) (b_i - \overline{B})}{n\sigma_A\sigma_B}. \tag{3.3}$$

However, on the next page on the last paragraph, it says

If $A$ and $B$ are independent (i.e., they do not have correlation), then ... $Cov(A,B) = \ldots = 0$.

Up to here, everything looks good, however by the following relation $$r_{A,B} = \frac{Cov(A,B)}{\sigma_A\sigma_B} \tag{3.5}$$ the correlation and covariance are related and as far as I remember, if the covariance of two random variables tend to be zero, it is not necessary that they are independent. However, the book says if $r_{A,B} = 0$ , then $A$ and $B$ are independent. Am I right that the book is wrong? or there is something else happening here.

• When all the correlations are 0 it is the off diagonal elements that should be 0. Zero correlation implies independence for a bivariate normal but not in general for other distrbutions. Apr 10, 2017 at 19:19
• Did you buy this book because it's required for a class? I can't think of other reasons to buy this thing Apr 10, 2017 at 19:19
• If you read just a little further in the book, it explicitly tells you that zero covariance does not imply independence: see the bottom of p. 97.
– whuber
Apr 10, 2017 at 19:38
• I will wholeheartedly agree that the book is not well written.
– whuber
Apr 10, 2017 at 19:46
• Possible duplicate of Under what additional conditions does independence follow from zero correlation? Apr 11, 2017 at 3:03

Zero correlation does not imply independence. Either:

1. There is a typo/mistake and the book is wrong or
2. The book made additional assumptions previously, for example, that the joint distribution of A and B were bivariate normal. There exist additional conditions such that zero correlation and these conditions would imply independence.
• The first is true. there are no other assumptions made previously. Apr 10, 2017 at 19:34

Your book is wrong. Correlation zero is not a sufficient condition for independence. You can have Pearson correlation zero for variables that are not independent.

The independent variables will have both covariance and correlation zero, provided their variances are non-zero. There's no contradiction here.