# B How is P(A∩B) = P(A)P(B) ?

1. Apr 22, 2017

If A and B are independent then the probability of both happening at once should be 0.
If we drew a ven diagram it'd be just two circles who don't intersect.

I'm guessing I got wrong definition of independent, can someone explain please?

2. Apr 22, 2017

### QuantumQuest

By definition, two events $A$ and $B$ are independent if the occurrence of one does not affect the probability of occurrence of the other. The concept of independence extends to more than two events, taking pairwise independence for every pair of events inside a finite set of events.

In order to understand why $P(A\cap B) = P(A)P(B)$ you can write this expression using conditional probabilities

$P(A) = \frac{P(A)P(B)}{P(B)} = \frac{P(A\cap B)}{P(B)} = P(A | B)$ and similarly for $P(B)$.

So, you can see in a more intuitive manner that the occurrence of one event does not affect the probability of occurrence of the other.

3. Apr 22, 2017

### Staff: Mentor

This is "mutually exclusive", not "independent". Think about it in terms of wagers. Suppose you are betting that it will rain tomorrow. Depending on your location you might accept 10:1 odds on such a wager.

Now, suppose that you also know that tomorrow is Tuesday, would that change the odds you would accept? If not, then they are independent.

If two events are mutually exclusive then knowing one definitely changes the wager you would accept on the other. So they are not independent.

4. Apr 22, 2017

### PeroK

If event A is Chelsea beating Spurs in the FA cup and event B is Arsenal beating Man City tomorrow, then those are independent, but they could both happen.

But, if event B was Spurs beating Chelsea, then events A and B are not independent, but mutually exclusive.

An example of two events that are dependent are event A that Arsenal win and event B that Sanchez scores a hat trick.

5. Apr 23, 2017

### PeroK

Although, as it turned out, one goal from Sanchez was enough!

6. Apr 24, 2017

### Demystifier

The most confusing thing here is the symbol $\cap$. The meaning of this symbol in set theory is very different from the meaning of the same symbol in probability theory. In set theory it can be interpreted in terms of Venn diagrams, but such an interpretation is not very useful in probability theory.

7. Apr 24, 2017

### Staff: Mentor

Why not? I usually consider it in terms of Venn diagrams also where the area of the region is the probability.

8. Apr 25, 2017

### Demystifier

Because it can mislead you to a wrong conclusion as in post #1.

9. Apr 25, 2017

### Staff: Mentor

But that is because he drew the Venn diagram wrong, not because a correctly drawn Venn diagram is not useful in probability.

10. Apr 26, 2017

### Demystifier

So how to correctly draw the Venn diagram in this case?

11. Apr 26, 2017

### Staff: Mentor

Draw a square of area 1, a circle of area P(A) and a circle of area P(B). Position them such that both circles are inside the square and their overlap has area P(A∩B). The shape of the circles can be distorted if needed.

12. Apr 26, 2017

### Demystifier

How such a diagram would tell us that A and B are independent?

13. Apr 26, 2017

### Staff: Mentor

You have it backwards. We were given that A and B were independent, and drew the diagram accordingly.

If instead we were given a Venn diagram where the area represents probability then you can simply check if P(A∩B) = P(A) P(B) by looking at the corresponding areas.

14. Apr 26, 2017

### Demystifier

OK, you can do it, but in my opinion it's not very useful. Do you know any reference where such diagrams are really used in practice?

15. Apr 26, 2017

### Staff: Mentor

Such diagrams were used to explain conditional probability and Bayes theorem to me when I was a student. Here is a lecture that takes the same approach: http://math.arizona.edu/~sreyes/math115as08/S08Proj1-BayesThm.ppt

I have not done a survey, but I have the impression that it is a common pedagogical technique. Certainly I would guess that the OP's teacher took that approach.

16. Apr 26, 2017

### PeroK

It's the way I remember Bayes' theorem.

$P(A|B) = \frac{P(B|A)P(A)}{P(B)}$

For some reason this is something I always find hard to remember. So, I draw a Venn diagram of two overlapping sets $A, B$ and note that:

$P(A|B) = \frac{P(A \cap B)}{P(B)}$

That's just the area of $A \cap B$ divided by the area of $B$.

Likewise:

$P(B|A) = \frac{P(A \cap B)}{P(A)}$

And then I put the two together.

One day, perhaps, I will memorise Bayes' Theorem directly!

17. Apr 26, 2017

### Demystifier

I still find the Venn diagrams in probability calculus more confusing than useful. But perhaps that's just me.

18. Apr 26, 2017

### FactChecker

Here are two example Venn diagrams of independent and dependent events. But I only use it to visualize the concept and don't know if it is a practical method for determining independence.