# Does covariance equal to zero implies independence for binary random variables?

If $X$ and $Y$ are two random variables that can only take two possible states, how can I show that $Cov(X,Y) = 0$ implies independence? This kind of goes against what I learned back in the day that $Cov(X,Y) = 0$ does not imply independence…

The hint says to start with $1$ and $0$ as the possible states and generalize from there. And I can do that and show $E(XY) = E(X)E(Y)$, but this doesn’t imply independence???

Kind of confused how to do this mathematically I guess.

If the two have zero covariance this means $E(XY) = E(X)E(Y)$, which means
It is trivial to see all other joint probabilities multiply as well, using the basic rules about independent events (i.e. if $A$ and $B$ are independent then their complements are independent, etc.), which means the joint mass function factorizes, which is the definition of two random variables being independent.