I have a question on subject chi-square test for independence.
I have, for example, two events A and B. If chi square test is not passed: is A dependent on B (A|B) or B on A (B|A)? Or does be valid both? (A|B and B|A).
Thank you in advance.
There's nothing wrong with the existing answers, but I suspect that you're looking for a causal sense of dependence rather than an associational one, that is: whether A causes B rather than whether B is more predictable when you know A. The chi^2 test is working with the second sense.
Even in the simplest case of the first kind of dependence you would ideally experimentally manipulate B and observe the effect on A and vice versa. Judea Pearl points out that this is the difference between the ordinary sense of conditional probability
P("I observe that A has value a" | "I observe that B has value b")
and a quite different thing that we might slightly misleadingly write as
P("I observe that A has value a" | "I fix B to have value b")
These need not, of course, be the same number.
I assume A and B are both random variables taking discrete values and you are thinking of a chi-squared test on the two-way frequency table formed by the counts of observations on the two variables.
In that case, a significant result indicates both directions of dependence: A|B and B|A.
If you think about Bayes' theorem, it is clear that one always implies the other:
P(A|B) = P(B|A) P(A) / P(B)
So P(A|B) = P(A) if and only if P(B|A)=P(B).
There are two definitions of statistical independence: 1) P(A,B)=P(A)*P(B) <=> 2) P(A|B)=P(A) <=> 2a) P(B|A)=P(B).
(<=> means if and only if)
So to answer your question: both are valid.
Pearson Chi-square test of independence is motivated by definition 1), logistic regression and multinomial regression are motivated by definition 2) of independence.
A chi-square test is not motivated by your description thus far. Give many more details.
Chi-square tests for independence are used to see if the probability or counts of each kind of event in a given variable are independent of other variables.