Q.A die is thrown. If E is the event 'the number appearing is a multiple of 3' and F be the event 'the number appearing is even' then find whether E and F are independent?
Concept understanding — Event Independence
Event Independence
Two events are independent when the occurrence of one does not change the probability of the other. Toss a coin and roll a die: the coin landing heads tells you nothing about whether the die shows a six. Contrast this with drawing cards without replacement, where the first draw does change the odds for the second — those events are dependent.
From Conditional Probability to a Clean Test
"Knowing B doesn't change A" means P(A∣B)=P(A). Substituting the definition P(A∣B)=P(B)P(A∩B) and clearing the fraction gives the symmetric form used in practice:
P(A∩B)=P(A)P(B).
Events A and B are independent exactly when the probability of both occurring equals the product of their individual probabilities. This version is preferred because it needs no non-zero condition and treats A and B alike.
A Quick Check
Roll a fair die. Let A={2,4,6} (even) and B={4,5,6} (greater than 3). Then P(A)=P(B)=21, and A∩B={4,6} so P(A∩B)=31. Since 31=21⋅21=41, these events are not independent.
Three or More Events
Events A,B,C are mutually independent only if all four conditions hold: the three pairwise products and
P(A∩B∩C)=P(A)P(B)P(C).
Pairwise independence alone is not enough to guarantee mutual independence.
Independent is not the same as mutually exclusive. Mutually exclusive events (with A∩B=∅) of non-zero probability are in fact strongly dependent: if one occurs the other cannot, so knowing one drastically changes the other's probability.
When events are independent, the multiplication rule simplifies to P(A∩B)=P(A)P(B), and it extends to any number of independent events. This is exactly what powers the binomial distribution and all repeated-trial problems.
The multiplication rule for independent events, P(A∩B) = P(A)P(B), is a core definition in the NCERT Class 12 Probability chapter and a near-guaranteed CBSE board and JEE Main question. "Independent events vs mutually exclusive events" is one of the most frequently searched probability confusions, and this distinction is tested almost every year in some form.
Concept: Event Independence — Two events E and F are independent if P(E∩F)=P(E)⋅P(F).
Step 1: Sample space for a die: S={1,2,3,4,5,6}.
E={3,6}, so P(E)=62=31.
F={2,4,6}, so P(F)=63=21.
Step 2: E∩F={6}, so P(E∩F)=61.
Step 3: Check product: P(E)⋅P(F)=31⋅21=61.
Since P(E∩F)=61=P(E)⋅P(F), the events satisfy the independence condition.
The events E and F are independent.
Two events are independent if P(E∩F)=P(E)⋅P(F). Here, P(E)=31, P(F)=21, and P(E∩F)=61. Since 61=31⋅21, the events are independent.
The core idea behind independence is simple: one event happening should not change the probability of the other. For a fair die, each face (1 through 6) is equally likely. So we can check this directly by comparing the product of individual probabilities with the probability of both happening together.
Let’s break it down.
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Define the sample space.
A single die throw gives S={1,2,3,4,5,6}, with each outcome having probability 61.
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Identify the events.
- E: number is a multiple of 3. Multiples of 3 in 1–6 are 3 and 6. So E={3,6}.
- F: number is even. Even numbers are 2, 4, 6. So F={2,4,6}.
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Compute individual probabilities.
- P(E)=∣S∣∣E∣=62=31.
- P(F)=∣S∣∣F∣=63=21.
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Find the intersection E∩F.
The common outcomes: 6 is both a multiple of 3 and even. So E∩F={6}.
Hence P(E∩F)=61.
-
Check the independence condition.
For independence, we need P(E∩F)=P(E)⋅P(F).
Compute the product:
P(E)⋅P(F)=31×21=61.
This matches P(E∩F)=61 exactly.
A common mistake is to think that because E and F share the outcome 6, they must be dependent. But independence is about probabilities, not just overlap. Here the overlap is exactly the size the product rule predicts.
- Interpret the result. Since the equality holds, E and F are independent events. Knowing the number is even does not change the chance that it is a multiple of 3, and vice versa.
You can also check using conditional probability: P(E∣F)=P(F)P(E∩F)=1/21/6=31=P(E). That’s another quick verification.
The events E and F are independent.
Method: Testing two events for independence
Use this whenever you must decide whether two events are independent — the answer is a single equality check, never a guess based on whether they overlap.
Steps
Step 1: Compute the three probabilities you need.
Find P(E), P(F) and the joint P(E∩F) by listing the sample space and the relevant subsets.
Step 2: Apply the product test.
Events are independent exactly when
P(E∩F)=P(E)P(F).
This symmetric form needs no non-zero condition and treats both events alike.
Step 3: Compare and conclude.
If the joint probability equals the product, the events are independent; if not, they are dependent. Sharing a common outcome does not by itself make events dependent — only the numerical equality decides. (You may double-check with P(E∣F)=P(E), which is equivalent.)
Common Mistakes
Mistake 1: Declaring the events dependent just because they share the outcome 6.
Why it's wrong: overlap does not decide independence — only the equality P(E∩F)=P(E)P(F) does. Correct approach: check P(E∩F)=61 against P(E)P(F)=31⋅21=61; they match, so the events are independent.
Mistake 2: Confusing independent with mutually exclusive.
Why it's wrong: mutually exclusive events cannot occur together, whereas these can (both happen at 6). Correct approach: use the product test, not disjointness, to judge independence.
Showing the 12 most recent of 55 on this concept.
- CBSE 2025Set 65/4/11 markMCQQ.Assertion (A) : If A and B are two events such that P(A∩B)=0, then A and B are independent events. Reason (R) : Two events are independent if the occurrence of one does not affect the occurrence of the other. (A) Both Assertion (A) and Reason (R) are true and Reason (R) is the correct explanation of Assertion (A). (B) Both Assertion (A) and Reason (R) are true, but Reason (R) is not the correct explanation of Assertion (A). (C) Assertion (A) is true, but Reason (R) is false. (D) Assertion (A) is false, but Reason (R) is true.
›Reveal solutionSolution
The assertion confuses mutually exclusive events (P(A∩B)=0) with independent events (P(A∩B)=P(A)⋅P(B)); the reason correctly defines independence. The answer is (D).
The heart of this question lies in distinguishing two fundamentally different relationships between events: mutual exclusivity and independence. These concepts are often confused because both involve restrictions on how events relate, but they describe opposite scenarios.
Understanding Independence
The reason (R) gives the correct intuitive definition: two events are independent when the occurrence of one does not affect the probability of the other occurring. Mathematically, events A and B are independent if and only if:
P(A∩B)=P(A)⋅P(B)
Equivalently, independence means P(A∣B)=P(A) (when P(B)>0) and P(B∣A)=P(B) (when P(A)>0). The events "don't care" about each other.
Why the Assertion Fails
Now let's examine what P(A∩B)=0 actually tells us.
1. What does P(A∩B)=0 mean?
This condition says that events A and B cannot occur simultaneously—they are mutually exclusive or disjoint. If one happens, the other cannot.
2. Testing for independence
For A and B to be independent, we need P(A∩B)=P(A)⋅P(B). If P(A∩B)=0, then independence requires:
0=P(A)⋅P(B)
This equation holds only if at least one of P(A) or P(B) equals zero—meaning at least one event is impossible.
3. The typical case
If both A and B have positive probabilities (both are possible events), then P(A)⋅P(B)>0. But we're told P(A∩B)=0. This means:
P(A∩B)=0=P(A)⋅P(B)
The events are not independent. In fact, they are maximally dependent: knowing one occurred tells you with certainty that the other did not.
Watch outMutually exclusive events with positive probabilities are always dependent, not independent. If A happens, it completely rules out B—that's maximum dependence, not independence!
4. A concrete example
Consider rolling a fair die. Let A = "rolling a 2" and B = "rolling a 5."
- P(A)=61, P(B)=61
- P(A∩B)=0 (can't roll both 2 and 5 simultaneously)
- For independence we'd need: P(A∩B)=61⋅61=361
But 0=361, so these events are not independent despite having P(A∩B)=0.
TipIndependence and mutual exclusivity are nearly opposite concepts. Independent events can happen together (with probability equal to the product of their individual probabilities), while mutually exclusive events cannot happen together at all.
Evaluating the Options
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Assertion (A): Claims P(A∩B)=0 implies independence. This is false (except in the trivial case where at least one event is impossible).
-
Reason (R): Correctly defines independence. This is true.
Since the assertion is false but the reason is true, the correct answer is (D).
✓Final answerThe correct option is (D): Assertion (A) is false, but Reason (R) is true.
- CBSE 2026Set 65/3/11 markMCQQ.If E and F are two independent events such that P(E)=103, P(E∪F)=21, then P(E∣F)−P(F∣E) is equal to: (A) 72 (B) 353 (C) 701 (D) 71
›Reveal solutionSolution
We use the property of independent events, P(E∩F)=P(E)P(F), along with the union formula to first find P(F). Then, we use the fact that for independent events, P(E∣F)=P(E) and P(F∣E)=P(F), to calculate the required difference. The final result is 701.
The core of this problem lies in understanding how the concept of "independent events" simplifies probability calculations, especially when dealing with unions and conditional probabilities.
When two events, E and F, are independent, it means that the occurrence of one event does not affect the probability of the other event occurring. This has two crucial implications:
- Intersection Probability: The probability of both E and F happening, P(E∩F), is simply the product of their individual probabilities: P(E∩F)=P(E)P(F).
- Conditional Probability: The probability of E happening given that F has already happened, P(E∣F), is just the probability of E, because F's occurrence doesn't change E's likelihood. So, P(E∣F)=P(E). Similarly, P(F∣E)=P(F).
We are given P(E), P(E∪F), and that E and F are independent. Our strategy will be to first use the formula for the union of events, combined with the independence property, to find P(F). Once we have P(F), we can directly use the independence property to find P(E∣F) and P(F∣E), and then calculate their difference.
-
Find P(F) using the union formula and independence.
The general formula for the probability of the union of two events is:
P(E∪F)=P(E)+P(F)−P(E∩F)
Since E and F are independent, we can substitute P(E∩F) with P(E)P(F):
P(E∪F)=P(E)+P(F)−P(E)P(F)
Now, substitute the given values: P(E)=103 and P(E∪F)=21.
21=103+P(F)−103P(F)
To solve for P(F), group the terms involving P(F):
21=103+P(F)(1−103)
21=103+P(F)(107)
Subtract 103 from both sides:
21−103=P(F)(107)
To subtract the fractions on the left, find a common denominator, which is 10:
105−103=P(F)(107)
102=P(F)(107)
Now, isolate P(F) by multiplying both sides by 710:
P(F)=102×710
P(F)=72
-
Calculate P(E∣F).
For independent events E and F, the probability of E occurring given that F has occurred is simply the probability of E. This is because F's occurrence provides no new information about E.
P(E∣F)=P(E)
We are given P(E)=103.
So, P(E∣F)=103.
-
Calculate P(F∣E).
Similarly, for independent events E and F, the probability of F occurring given that E has occurred is simply the probability of F.
P(F∣E)=P(F)
From Step 1, we found P(F)=72.
So, P(F∣E)=72.
-
Calculate P(E∣F)−P(F∣E).
Now, substitute the values we found:
P(E∣F)−P(F∣E)=103−72
To subtract these fractions, find a common denominator, which is 70:
103−72=10×73×7−7×102×10
=7021−7020
=701
✓Final answerThe value of P(E∣F)−P(F∣E) is 701.
- CBSE 2026Set V11 markMCQQ.The probability of obtaining an even prime number on each die when a pair of dice is rolled(a) 361(b) 61(c) 181(d) 41
›Reveal solutionSolution
The even prime is 2; P(2 on each of two dice)=61⋅61=361; answer (a).
The only even prime number is 2. For one die, P(show 2)=61. The two dice are independent, so
P(even prime on each)=61×61=361.
✓Final answer(a) 361
- CBSE 2026Set V11 markMCQQ.If A and B are independent events with P(A)=0.3 and P(B)=0.4 then P(A∩B)(a) 1.2(b) 0.12(c) 0.7(d) 43
›Reveal solutionSolution
Independence gives P(A∩B)=P(A)P(B)=0.12; answer (b).
For independent events A and B,
P(A∩B)=P(A)⋅P(B)=0.3×0.4=0.12.
(Note (a) 1.2 is impossible since a probability cannot exceed 1.)
✓Final answer(b) 0.12
- CBSE 2026Set A1 markMCQQ.If A, B and C are three independent events then P(ABC)=(a) P(A)+P(B)+P(C)(b) P(A)−P(B)−P(C)(c) P(A)⋅P(B)⋅P(C)(d) None of these
›Reveal solutionSolution
For independent events, P(A∩B∩C)=P(A)P(B)P(C).
By definition, events A, B, C are (mutually) independent when the probability of their joint occurrence equals the product of their individual probabilities:
P(ABC)=P(A)⋅P(B)⋅P(C).
Adding probabilities (option a) applies to mutually exclusive unions, not to intersections of independent events.
✓Final answer(c) P(A)⋅P(B)⋅P(C).
- CBSE 2026Set ANNUAL1 markMCQQ.If A and B are independent events and P(A)=0.3 and P(B)=0.4, then the value of P(A∪B) will be(a) 0.58(b) 0.70(c) 0.12(d) 0.10
›Reveal solutionSolution
For independent events, P(A∩B)=P(A)P(B), and the addition rule gives P(A∪B).
P(A∩B)=0.3×0.4=0.12 (independence).
P(A∪B)=P(A)+P(B)−P(A∩B)=0.3+0.4−0.12=0.58.
✓Final answerThe correct option is (a) 0.58.
- CBSE 2026Set ANNUAL1 markMCQQ.The probability of obtaining an even prime number on each dice, when a pair of dice is rolled, is:(a) 0(b) 31(c) 121(d) 361
›Reveal solutionSolution
The only even prime number is 2, so we need a 2 on each die.
Among {1,2,3,4,5,6}, the only even prime is 2.
P(2 on one die)=61
Since the two dice are independent, P(2 on both dice)=61×61=361
✓Final answerOption (d): 361
- CBSE 2026Set ANNUAL1 markMCQQ.Ajay and Meera are contesting for two vacancies in a company. Probability of selection of Ajay is 7/9 and that of Meera is 4/7. What is the probability that both will be rejected?(a) 61/63(b) 6/63(c) 41/63(d) 28/63
›Reveal solutionSolution
The rejection probabilities of each candidate are complements of their selection probabilities; since the two events are independent, multiply them.
P(Ajay selected)=97⟹P(Ajay rejected)=1−97=92
P(Meera selected)=74⟹P(Meera rejected)=1−74=73
Since selection of Ajay and Meera are independent events:
P(both rejected)=92×73=636
✓Final answerThe probability that both are rejected is 636 (option b).
- CBSE 2026Set ANNUAL1 markMCQQ.Assertion (A): Two events A and B are such that P(A) = 1/4, P(B) = 1/2 and P(A∩B) = 1/8 then two events A and B are independent. Reason (R): Two events are independent if the probability of occurrence of one does not affect the probability of occurrence of other and P(A∩B) = P(A) + P(B) − P(A∪B)(a) Both Assertion (A) and Reason (R) are true and Reason (R) is the correct explanation of Assertion (A)(b) Both Assertion (A) and Reason (R) are true, but Reason (R) is not the correct explanation of Assertion (A)(c) Assertion (A) is true but Reason (R) is false.(d) Both Assertion (A) and Reason (R) are false.
›Reveal solutionSolution
The independence check in A is numerically correct, but R states the wrong criterion — it gives the addition-rule identity (true for ANY two events), not the actual independence condition P(A∩B)=P(A)⋅P(B).
Checking Assertion (A): Independence requires P(A∩B)=P(A)⋅P(B).
P(A)⋅P(B)=41×21=81,
which equals the given P(A∩B)=1/8. So A and B are independent — A is true.
Checking Reason (R): R correctly describes independence in words ("occurrence of one does not affect the other"), but then states the test as
P(A∩B)=P(A)+P(B)−P(A∪B).
This is just the general addition-rule identity, rearranged — it holds for every pair of events, independent or not, and is NOT the criterion that establishes independence (that criterion is P(A∩B)=P(A)P(B), which R never states). So as a stated reason for independence, R is false/incorrect.
✓Final answerAssertion (A) is true but Reason (R) is false. (Option c)
- CBSE 2026Set ANNUAL1 markMCQQ.Let E and F be events with P(E)=31, P(F)=21 and P(E∩F)=61. Then(a) E and F are independent events(b) E and F are mutually exclusive events(c) E and F are disjoint events(d) None of the above
›Reveal solutionSolution
Two events are independent exactly when P(E∩F)=P(E)⋅P(F); check whether the given numbers satisfy this.
Given P(E)=31, P(F)=21, P(E∩F)=61.
Test for independence:
P(E)⋅P(F)=31×21=61
This equals the given P(E∩F)=61. Since P(E∩F)=P(E)P(F), E and F are independent.
Rule out the other options: mutually exclusive/disjoint events would require P(E∩F)=0, but here P(E∩F)=61=0, so E and F are not mutually exclusive (and "disjoint" means the same thing as mutually exclusive).
✓Final answer(a) E and F are independent events
- CBSE 2026Set ANNUAL1 markQ.If A and B are two independent events with P(A) = 1/2 and P(B) = 1/3, then find P(A ∪ B).
›Reveal solutionSolution
For independent events, P(A∩B)=P(A)P(B); then apply the addition rule.
P(A∪B)=P(A)+P(B)−P(A)P(B)=21+31−21⋅31=21+31−61=63+2−1=64=32
✓Final answerP(A∪B)=2/3.
- CBSE 2026Set ANNUAL1 markQ.Write the answer in one word/sentence: If E and F are independent events then write the value of P(E∩F).
›Reveal solutionSolution
For independent events, the probability of the intersection is the product.
By definition, events E and F are independent iff P(E∩F)=P(E)P(F).
✓Final answerP(E∩F)=P(E)P(F).
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