Statistics

Probability Rules: Addition & Multiplication

Probability has a few core rules that let you combine events. Want to know the chance of A or B happening? That’s the addition rule. Want A and B? That’s the multiplication rule. These two rules, once mastered, let you solve almost any probability problem.


Part 1: The Addition Rule

The probability of A or B (or both) happening is:

P(AB)=P(A)+P(B)P(AB)P(A \cup B) = P(A) + P(B) - P(A \cap B)

Why subtract P(A and B)? Because when we add P(A) and P(B), we double-count the overlap — outcomes where both happen. We need to subtract it once to fix that.

Visualize A and B as two overlapping curves. The total “or” probability is everything under either curve, but the overlap region shouldn’t be counted twice:

P(A)0.4
0.10.7
P(B)0.5
0.10.7
P(A and B) — overlap0.15
00.4
P(AB)=0.4+0.50.15P(A \cup B) = 0.4 + 0.5 - 0.15
-8-6-4-22468Event AEvent BA and B (overlap)
Try This

Try setting the overlap to 0. When P(A and B) = 0, the events are mutually exclusive — they can’t both happen. The formula simplifies to: P(A or B) = P(A) + P(B). Notice how the curves don’t overlap at all!


Part 2: Mutually Exclusive Events

When two events cannot happen at the same time (like rolling a 2 and a 5 on one die), they are mutually exclusive. Their overlap is zero:

Mutually exclusive: P(AB)=0\text{Mutually exclusive: } P(A \cap B) = 0
Separation between events3
15
-10-8-6-4-2246810Event AEvent B

As you increase the separation, the curves move apart and the overlap vanishes. Completely separated = mutually exclusive. For these events, simple addition works:

P(AB)=P(A)+P(B)=0.4+0.4=0.8P(A \cup B) = P(A) + P(B) = 0.4 + 0.4 = 0.8

Part 3: The Multiplication Rule

For independent events, the probability of both happening is:

P(AB)=P(A)×P(B)P(A \cap B) = P(A) \times P(B)

Think of a tree diagram: first A happens (or doesn’t), then B happens (or doesn’t). Each branch multiplies the probabilities along its path.

Let’s model two independent events with adjustable probabilities:

P(A)0.6
0.050.95
P(B)0.7
0.050.95
P(AB)=P(A)×P(B)=0.6×0.7P(A \cap B) = P(A) \times P(B) = 0.6 \times 0.7
-8-6-4-22468P(A)P(B)P(A and B)
Connection

Tree diagram thinking: Imagine flipping two coins.

  • P(heads on first) = 0.5
  • P(heads on second) = 0.5
  • P(both heads) = 0.5 * 0.5 = 0.25

Each path through the “tree” multiplies the branch probabilities. Set both sliders to 0.5 and verify the joint probability shows 0.25!


Part 4: Dependent Multiplication Rule

When events are not independent, the multiplication rule uses conditional probability:

P(AB)=P(A)×P(BA)P(A \cap B) = P(A) \times P(B \mid A)

The probability of B changes depending on whether A happened:

P(A)0.5
0.10.9
P(B|A)0.8
0.10.9
P(AB)=0.5×0.8P(A \cap B) = 0.5 \times 0.8
-8-6-4-22468P(A)P(B|A)P(A and B)
Try This

Card example: Draw 2 cards from a deck without replacement.

  • P(first is Ace) = 4/52
  • P(second is Ace | first was Ace) = 3/51 (one ace is gone!)
  • P(both Aces) = (4/52) * (3/51) = 12/2652

Notice how P(B|A) is smaller than P(B) would be alone — the first draw removed an ace from the deck. That’s dependence in action.


Part 5: Complement Rule

Sometimes it’s easier to calculate what you don’t want and subtract:

P(A)=1P(not A)P(A) = 1 - P(\text{not } A)
P(A)0.3
01
P(not A)=10.3P(\text{not } A) = 1 - 0.3
-8-6-4-22468P(A)P(not A)
Challenge

Challenge: You roll a die 3 times. What’s the probability of getting at least one 6?

Hint: It’s easier to find P(no sixes at all) and subtract from 1. P(not 6 on one roll) = 5/6. For three independent rolls: P(no sixes) = (5/6)^3. So P(at least one 6) = 1 - (5/6)^3.

Calculate it, then verify: is it higher or lower than you’d guess?


Wrapping Up

RuleFormulaWhen to Use
AdditionP(A or B) = P(A) + P(B) - P(A and B)Either event happening
Mutually ExclusiveP(A or B) = P(A) + P(B)Events can’t co-occur
Multiplication (independent)P(A and B) = P(A) * P(B)Both events, no influence
Multiplication (dependent)P(A and B) = P(A) * P(B|A)Both events, with influence
ComplementP(A) = 1 - P(not A)“At least one” problems

These five rules are your complete toolkit. Every probability problem you’ll encounter in this course can be broken down into combinations of these rules.

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