IB Maths AI HL Probability Paper 1 & 2 ~7 min read

Conditional Probability

Conditional probability is the chance of one event happening given that another has already happened โ€” written P(A|B), “A given B“. The “given” information shrinks your sample space: you only look at the cases where B happened, then ask what fraction of those also have A. This is exactly what powers “without replacement” problems, where each pick changes the odds for the next.

๐Ÿ“˜ What you need to know

The idea: a shrinking sample space

The word “given” tells you to ignore everything outside B. Your new total is the B group; you count how many of those are also A.

P(A|B) โ€” restrict to B, then find the A part
U A B all of B = denominator U A B A โˆฉ B = numerator
P(A|B) = “double-shaded” (A โˆฉ B) รท “all of B“. The given event becomes the new whole.
Conditional probability P(A|B) = P(A โˆฉ B)P(B) in the formula booklet โœ“

๐Ÿง  Memory aid โ€” “double shading over all shading”

On a Venn diagram, P(A|B) = double-shaded รท all of the B-shading. The condition B is the bottom (the new total); the overlap A โˆฉ B is the top. With raw counts: out of the B group, what fraction are also A?

Counting method (often fastest)

When you have actual numbers, you don’t even need the formula โ€” just restrict to the given group and count.

๐Ÿงญ Recipe โ€” P(A|B) from counts

  1. Find the B group โ€” how many are in B? That’s your denominator.
  2. Of those, how many are also in A? That’s your numerator.
  3. Divide: P(A|B) = n(A โˆฉ B)n(B).
Without replacement is conditional in disguise: after taking one item, the total drops by 1 (and the count of that type drops too), so the next probability is conditioned on the first pick.

๐Ÿค” Why does P(A|B) usually differ from P(B|A)?

They have different denominators. P(A|B) divides the overlap by P(B); P(B|A) divides the same overlap by P(A). Same top, different bottom โ†’ different answers (unless P(A) = P(B)). So “A given B” and “B given A” are not interchangeable โ€” read the wording carefully.

Worked examples

WE 1

Basic probability from a class

In a class of 30: 19 have a dog, 17 have a cat, 11 have both. A student is chosen at random. Find P(has a dog).

P(D) = n(D)/n(U) = 19/30 P(D) = 19/30 full sample space here is all 30 students.
WE 2

Conditional โ€” dog given cat

Using the same class, find P(has a dog given they have a cat).

restrict to the cat group 17 have a cat (new denominator) of those, how many also have a dog? 11 have both P(D|C) = 11/17 “given cat” โ†’ divide by the 17 cat-owners, not 30.
WE 3

Conditional โ€” cat given dog

For the same class, find P(has a cat given they have a dog).

restrict to the dog group 19 have a dog (new denominator) of those, how many also have a cat? 11 have both P(C|D) = 11/19 note P(C|D) โ‰  P(D|C) โ€” different denominators.
WE 4

Use the formula

P(A โˆฉ B) = 0.24 and P(B) = 0.4. Find P(A|B).

P(A|B) = P(A โˆฉ B) / P(B) = 0.24 / 0.4 P(A|B) = 0.6 straight substitution into the formula.
WE 5

Without replacement

A bag has 10 balls, 6 red. Two are taken without replacement. Given the first is red, find P(second is red).

one red already removed balls left: 9, reds left: 5 probability next is red = 5/9 P(2nd red | 1st red) = 5/9 the total and red count both drop โ€” that’s the conditioning.

๐Ÿ’ก Top tips

โš  Common mistakes

Next up โ€” Venn Diagrams. You’ve used Venn regions to picture P(A|B); next you’ll fill them in properly. Working from the centre out (intersections first), you’ll lay out frequencies or probabilities, read off โˆฉ, โˆช, โ€ฒ and conditional values directly, and use algebra when a region is unknown.

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