Independence of Features assumption in Naive Bayes
Statistical independence is a pretty straightforward thing. If $$p(A\cap B) = p(A) p(B)$$ then $A$ and $B$ are independent (in other words if marginal distributions are equal to conditional). If you want, you could even check that on your data. Though it would be easier to check:
$$p(A|B) = p(A) \ \text{and} \  p(B|A) = p(B)$$ instead of constructing a joint distribution. The latter is easy, if your features are categorical then you could estimate  $p(A)$, $p(B)$ , $p(A|B)$, $P(B|A)$ as sample frequencies. If one of A or B is categorical computations are also simple. If both A and B are numeric, you need to fit a KDE (kernel density estimation) model to all probability distributions.
However, on practice it is simpler and faster just to fit a Naive Bayes and check its performance on a test set.