Since P(A | B) ≠ P(A), the occurrence of event B the probability that event A will occur. This implies that A and B are not independent events.

The statement you've provided relates to the concept of independence in probability theory. To elaborate:

1. Conditional Probability: $P(A | B)$ is the probability of event A occurring given that event B has already occurred.

2. Marginal Probability: $P(A)$ is the overall probability of event A occurring without considering any other events.

3. Independence: Two events A and B are independent if and only if the occurrence of B does not influence the occurrence of A. Mathematically, this means that $P(A | B) = P(A)$.

Given that $P(A | B) \neq P(A)$, it indicates that knowing event B has occurred changes the probability of event A occurring, thereby confirming that A and B are dependent events.

Conclusion:

This dependency implies a relationship between the two events where the occurrence of one affects the likelihood of the other. In practical terms, recognizing whether events are independent is crucial in fields such as statistics, risk assessment, and data analysis, as it can influence decision-making and predictions.