Independent Events: Formula, Rules and Examples

Independent Events are events where the occurrence of one event does not change the probability of the other. This page explains the multiplication rule, conditional probability test, complement method, difference between independent and mutually exclusive events, and solved probability examples for aptitude questions.

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What Are Independent Events?

Two events A and B are independent if the occurrence of A does not affect the probability of B, and vice versa. Mathematically, P(A ∩ B) = P(A)P(B).

For independent events, P(B | A) = P(B) and P(A | B) = P(A), provided the conditioning events have positive probability. For example, tossing a coin and rolling a die are independent because the coin result does not change the die result. The probability of getting a head and an even number is 1/2 × 3/6 = 1/4.

Independent Events Video Lessons

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Independent Events and Multiplication Theorem

Learn how to identify independent events and apply the multiplication theorem to calculate the probability of their joint occurrence.

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Independent Events Formula & Tricks

Important Formulas

Multiplication formula
P(A ∩ B) = P(A) × P(B)

Use this when A and B are independent and the question asks for both events to occur.

Conditional probability test
P(A | B) = P(A) and P(B | A) = P(B)

These equalities indicate that the occurrence of one event does not change the probability of the other.

Complement of independent events
P(Aᶜ ∩ Bᶜ) = [1 − P(A)][1 − P(B)]

The complements of independent events are also independent.

At least one event occurs
P(A ∪ B) = P(A) + P(B) − P(A)P(B)

For independent events, substitute P(A ∩ B) = P(A)P(B) into the addition rule.

Quick Tricks

Use the complement for ‘at least one’

For several independent events, calculate the probability that none occurs and subtract it from 1.

Example: If the success probabilities in two independent trials are 1/3 and 1/4, P(at least one success) = 1 − (2/3 × 3/4) = 1/2.
Check independence before multiplying

Do not multiply probabilities directly unless the events are stated to be independent or the conditional probability confirms independence.

Example: Drawing two cards without replacement is generally dependent because the first draw changes the composition of the deck.
Separate repeated independent trials

For n independent trials with success probability p, the probability of exactly r successes is C(n,r)pʳ(1 − p)ⁿ⁻ʳ.

Example: For exactly two heads in three fair tosses, C(3,2)(1/2)²(1/2) = 3/8.

Independent Events Concepts

Multiplication Rule for Independent Events

The probability that two independent events both occur is the product of their individual probabilities.

If P(A) = a and P(B) = b, then P(A ∩ B) = ab. For three mutually independent events, P(A ∩ B ∩ C) = P(A)P(B)P(C).

Example: A fair die is rolled and a fair coin is tossed. P(getting 6 and head) = 1/6 × 1/2 = 1/12.

Testing Whether Events Are Independent

Events A and B are independent if P(A ∩ B) = P(A)P(B).

Calculate the intersection probability and compare it with the product of the individual probabilities. If the values are equal, the events are independent; otherwise, they are dependent. Equivalently, test whether P(A | B) = P(A), when P(B) is non-zero.

Example: If P(A) = 1/2, P(B) = 2/5 and P(A ∩ B) = 1/5, then P(A)P(B) = 1/2 × 2/5 = 1/5. Hence, A and B are independent.

Independent Events and Complements

If A and B are independent, then Aᶜ and B, A and Bᶜ, and Aᶜ and Bᶜ are also independent.

The complement rule gives P(Aᶜ) = 1 − P(A). Thus, for independent A and B, P(Aᶜ ∩ B) = [1 − P(A)]P(B), and P(Aᶜ ∩ Bᶜ) = [1 − P(A)][1 − P(B)].

Example: If P(A) = 0.6 and P(B) = 0.5, then P(Aᶜ ∩ Bᶜ) = 0.4 × 0.5 = 0.2.

Independent Versus Mutually Exclusive Events

Independent events can occur together, whereas mutually exclusive events cannot occur together.

For mutually exclusive events, P(A ∩ B) = 0. If both events have positive probabilities, they cannot also be independent because P(A)P(B) is positive. Therefore, non-zero mutually exclusive events are dependent.

Example: In one die roll, getting an odd number and getting an even number are mutually exclusive. They are not independent because their intersection has probability 0, while (1/2)(1/2) = 1/4.

Independent Events Data Table

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Independent and Mutually Exclusive Events

Comparison of the intersection rule and occurrence relationship.

Property Independent events Mutually exclusive events
Can both occur? Yes, generally No
Intersection probability P(A ∩ B) = P(A)P(B) P(A ∩ B) = 0
Effect of one event Does not change the other probability Occurrence of one rules out the other
Can both conditions hold? Only if at least one event has probability 0 Not when both probabilities are positive

Note: Mutually exclusive events with positive probabilities are dependent.

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Practice Independent Events Questions

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Quick Revision Notes

Independent Events Revision Points

Recall the following definitions, formulas and checks.

  • Independent events satisfy P(A ∩ B) = P(A)P(B).
  • Equivalent tests are P(A | B) = P(A) and P(B | A) = P(B), when defined.
  • For independent events, P(A ∪ B) = P(A) + P(B) − P(A)P(B).
  • Complements of independent events are also independent.
  • For n independent trials, exactly r successes have probability C(n,r)pʳ(1 − p)ⁿ⁻ʳ.
  • Mutually exclusive events with positive probabilities are not independent.

Independent Events FAQs

What is the formula for two independent events?

The formula is P(A ∩ B) = P(A)P(B), where A ∩ B means that both events occur.

If P(A) = 0.4 and P(B) = 0.5, what is P(A and B) when the events are independent?

P(A ∩ B) = 0.4 × 0.5 = 0.20.

How can independence be checked from given probabilities?

Compare P(A ∩ B) with P(A)P(B). The events are independent only when these two values are equal.

Are mutually exclusive events always independent?

No. If mutually exclusive events both have positive probabilities, P(A ∩ B) = 0 but P(A)P(B) > 0, so they are dependent.

What is the probability of at least one success in three independent trials?

If each trial has success probability p, the probability is 1 − (1 − p)³.

What is the probability of exactly two successes in four independent trials?

If each trial has success probability p, the probability is C(4,2)p²(1 − p)² = 6p²(1 − p)².

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