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CBSE Class 12 Mathematics Solutions for Chapter 13 Probability – NCERT Exercise 13.1 [2025-26]

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Download Free PDF of Probability Exercise 13.1 for Class 12 Maths NCERT Solutions

If you’re aiming to strengthen your foundation in Class 12 Maths, focusing on Probability is vital—especially for mastering exam questions. NCERT Solutions for Class 12 Maths Chapter 13 Probability Exercise 13.1 help you tackle every sum step by step, clarifying concepts like sample space and event probability calculation. These solutions match the patterns found in recent board exams and follow the latest CBSE syllabus guidelines, so you’re fully prepared.

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Probability regularly appears in board papers, often carrying a weightage of 8 marks for Class 12, so practicing this exercise can directly boost your scores. Many students search for “exercise 13.1 class 12” or “probability class 12 ncert solutions” when revising under time pressure. Our resource targets the core skills you need—distinguishing between types of events and applying the classical probability formula to questions.


Every step and formula here is quality-checked by Vedantu’s expert educators. This means reliable content you can trust in your revision. Be sure to review the Class 12 Maths syllabus if you want to confirm complete board alignment before diving in.

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Access NCERT Solutions for Maths Class 12 Chapter 13 - Probability

Exercise 13.1

1. Given that E and F are events such that $P\left( E \right)=0.6$, and $P\left( F \right)=0.3  $\[P\left( E\cap F \right)=0.2\], find P(E|F) and P(F|E). 

Ans: It is given in the question that $P\left( E \right)=0.6$ = 0.6, $P\left( F \right)=0.3$, and $P\left( E\cap F \right)=0.2$

Now P(E|F) is given by

\[\text{     }P\left( E|F \right)=\frac{P\left( E\cap F \right)}{P\left( F \right)}\]

$\Rightarrow P\left( E|F \right)=\frac{0.2}{0.3}$

Hence we found that 

$P\left( E|F \right)=\frac{2}{3}$

With a similar idea and the same formula we can proceed to find P(F|E) as shown

\[P\left( F|E \right)=\frac{P\left( F\cap E \right)}{P\left( E \right)}\]

Now we know that\[P\left( F\cap E \right)\] and $P\left( E\cap F \right)$ is same 

$\therefore P\left( F|E \right)=\frac{0.2}{0.6}$

Hence we found that 

$P\left( F|E \right)=\frac{1}{3}$

Thus $P\left( E|F \right)=\frac{2}{3}$ and $P\left( F|E \right)=\frac{1}{3}$


2. Compute P(A|B), if $P\left( B \right)=0.5$ and $P\left( A\cap B \right)=0.32$

Ans: Given in the question  $P\left( B \right)=0.5$ and $P\left( A\cap B \right)=0.32$

So to find P(A|B we use the formula 

\[P\left( A|B \right)=\frac{P\left( A\cap B \right)}{P\left( B \right)}\]

\[P\left( A|B \right)=\frac{0.32}{0.5}\]

$\frac{32}{50}$

$\frac{16}{25}$

Hence we found that \[P\left( A|B \right)=\frac{16}{25}\]


3. If  $P\left( A \right)=0.8$, $P\left( B \right)=0.5$ and $\mathbf{P}\left( B|A \right)=0.4$ find

(i) $P\left( A\cap B \right)$

Ans: It is given that $P\left( A \right)=0.8$, $P\left( B \right)=0.5$, and \[P\left( B|A \right)=0.4\]

Put all the data in the following formula

\[\text{      }P\left( B|A \right)=\frac{P\left( A\cap B \right)}{P\left( A \right)}\]

$\Rightarrow P\left( A\cap B \right)=P\left( A|B \right).P\left( B \right)$
$\Rightarrow P\left( A\cap B \right)=0.4\times 0.8$

Thus we found that $P\left( A\cap B \right)=0.32$

(iii) $\mathbf{P}\left( A|B \right)$

Ans: Given in the question $P\left( A \right)=0.8$, $P\left( B \right)=0.5$, and \[P\left( B|A \right)=0.4\]

We know that \[P\left( A|B \right)\]is the probability of occurrence of A when B has already happened

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

Now put $P\left( A\cap B \right)=0.32$, $P\left( B \right)=0.5$in the above equation as shown

$\Rightarrow P\left( A|B \right)=\frac{0.32}{0.5}$

$=0.64$

Thus we found that $P\left( A|B \right)=0.64$

(iii) $\mathbf{P}\left( A\bigcup B \right)$

Ans: Given in the question $P\left( A \right)=0.8$, $P\left( B \right)=0.5$, $P\left( B \right)=0.5$and \[P\left( B|A \right)=0.4\]

Now we have the formula as shown

$\text{  }P\left( A\bigcup B \right)=P\left( A \right)+P\left( B \right)-P\left( A\cap B \right)$

Put $\text{   }P\left( A \right)=0.8$, 

$\text{         }P\left( B \right)=0.5$, 

$\text{  }P\left( A\cap B \right)=0.32$ in the above as shown

$\therefore P\left( A\bigcup B \right)=0.8+0.5-0.32$

$\Rightarrow P\left( A\bigcup B \right)=0.98$

Thus we found that $P\left( A\bigcup B \right)=0.98$


4. Evaluate $\mathbf{P}\left( A\bigcup B \right)$ if $2\mathbf{P}\left( A \right)=P\left( B \right)=\frac{5}{13}$ and $P\left( A\left| B \right. \right)=\frac{2}{5}$

Ans: It is Given that 

$2P\left( A \right)=P\left( B \right)=\frac{5}{13}$ and 

$P\left( A\left| B \right. \right)=\frac{2}{5}$

$\Rightarrow P\left( A \right)=\frac{5}{26}$

Now we know the formula 

$\text{       }P\left( A\left| B \right. \right)=\frac{P\left( A\cap B \right)}{P\left( B \right)}$

$\Rightarrow \text{            }\frac{2}{5}=\frac{13P\left( A\cap B \right)}{5}$ (since $P\left( B \right)=\frac{5}{13}$, $P\left( A\left| B \right. \right)=\frac{2}{5}$)

$\Rightarrow P\left( A\cap B \right)=\frac{2}{13}$

Also $P\left( A\bigcup B \right)$ is given by the formula 

$\text{  }P\left( A\bigcup B \right)=P\left( A \right)+P\left( B \right)-P\left( A\cap B \right)$

$\Rightarrow P\left( A\bigcup B \right)=\frac{5}{26}+\frac{5}{13}-\frac{2}{13}$

$\Rightarrow P\left( A\bigcup B \right)=\frac{11}{26}$

Thus we found that $P\left( A\bigcup B \right)=\frac{11}{26}$


5. If $\mathbf{P}\left( A \right)=\frac{6}{11}$, $\mathbf{P}\left( B \right)=\frac{5}{11}$ and $\mathbf{P}\left( A\bigcup B \right)=\frac{7}{11}$ find

(i) $\mathbf{P}\left( A\cap B \right)$

Ans: Given $P\left( A \right)=\frac{6}{11}$,  $P\left( B \right)=\frac{5}{11}$ 

Also it is given that

 $P\left( A\bigcup B \right)=\frac{7}{11}$

And we know that it is given by the formula

$\text{   }P\left( A\bigcup B \right)=P\left( A \right)+P\left( B \right)-P\left( A\cap B \right)$

$\text{          }\Rightarrow \frac{7}{11}=\frac{6}{11}+\frac{5}{11}-P\left( A\cap B \right)$

$\Rightarrow P\left( A\cap B \right)=\frac{11}{11}-\frac{7}{11}$

$\Rightarrow P\left( A\cap B \right)=\frac{4}{11}$

Thus we found that $P\left( A\cap B \right)=\frac{4}{11}$

(ii) $\mathbf{P}\left( A\left| B \right. \right)$

Ans: Given $P\left( A \right)=\frac{6}{11}$,  $P\left( B \right)=\frac{5}{11}$

Also we know that $P\left( A\left| B \right. \right)$ is given by 

$\text{    }P\left( A\left| B \right. \right)=\frac{P\left( A\cap B \right)}{P\left( B \right)}$

\[\Rightarrow P\left( A\left| B \right. \right)=\frac{4}{11}\times \frac{11}{5}\] (since $P\left( A\cap B \right)=\frac{4}{11}$)

Thus we found that \[P\left( A\left| B \right. \right)=\frac{4}{5}\]

(iii) $\mathbf{P}\left( B\left| A \right. \right)$

Ans: Given $P\left( A \right)=\frac{6}{11}$,  $P\left( B \right)=\frac{5}{11}$

Also we know that $P\left( B\left| A \right. \right)$ is given by

 $\text{    }P\left( B\left| A \right. \right)=\frac{P\left( A\cap B \right)}{P\left( A \right)}$

$\Rightarrow P\left( B\left| A \right. \right)=\frac{4}{11}\times \frac{11}{6}$

Thus we found that $P\left( B\left| A \right. \right)=\frac{2}{3}$


6. A coin is tossed three times, where

(i) E: head on third toss, F: heads on first two tosses

Ans: Sample space is given by

$S=\left\{ \left. HHH, HHT, HTH, HTT, THH, THT, TTH, TTT \right\} \right.$and the events E and F and their probabilities are given by

$E=\left\{ \left. HHH, HTH, THH, TTH \right\} \right.$

Therefore $P\left( E \right)=\frac{4}{8}$

$F=\left\{ \left. HHH,HHT \right\} \right.$

Therefore $P\left( F \right)=\frac{2}{8}$

$\therefore E\cap F=\left\{ \left. HHH \right\} \right.$

Therefore $P\left( E\cap F \right)=\frac{1}{8}$

And hence $P\left( E\left| F \right. \right)$ is given by

$\text{    }P\left( E\left| F \right. \right)=\frac{P\left( E\cap F \right)}{P\left( F \right)}$

\[\Rightarrow P\left( E\left| F \right. \right)=\frac{\frac{1}{8}}{\frac{2}{8}}\]

Thus \[P\left( E\left| F \right. \right)=\frac{1}{2}\]

(ii) E: at least two heads, F: at most two heads

Ans: Sample space is given by

$S=\left\{ \left. HHH, HHT, HTH, HTT, THH, THT, TTH, TTT \right\} \right.$and the events E and F and their probabilities are given by

$E=\left\{ \left. HHH, HHT, THH, HTH \right\} \right.$

Therefore $P\left( E \right)=\frac{4}{8}$

$F=\left\{ \left. HHT, HTH, HTT, THH, THT, TTH, TTT \right\} \right.$

Therefore $P\left( F \right)=\frac{7}{8}$

$E\cap F=\left\{ \left. HHT, HTH, THH \right\} \right.$

Therefore $P\left( E\cap F \right)=\frac{3}{8}$

And hence $P\left( E\left| F \right. \right)$ is given by

$\text{    }P\left( E\left| F \right. \right)=\frac{P\left( E\cap F \right)}{P\left( F \right)}$

\[\Rightarrow P\left( E\left| F \right. \right)=\frac{\frac{3}{8}}{\frac{7}{8}}\]

Thus \[P\left( E\left| F \right. \right)=\frac{3}{7}\]

(iii) E: at most two tails, F: at least one tail.

Ans: Sample space is given by

$S=\left\{ \left. HHH, HHT, HTH, HTT, THH, THT, TTH, TTT \right\} \right.$and the events E and F and their probabilities are given by

$E=\left\{ \left. HHH, HHT, THH, HTH, THH, THT, TTH \right\} \right.$

Therefore $P\left( E \right)=\frac{7}{8}$

$F=\left\{ \left. HHT, HTT, HTH, THH, THT, TTH, TTT \right\} \right.$

Therefore $P\left( F \right)=\frac{7}{8}$

$E\cap F=\left\{ \left. HHT, HTT, HTH, THH, THT, TTH \right\} \right.$

Therefore $P\left( E\cap F \right)=\frac{6}{8}$

And hence $P\left( E\left| F \right. \right)$ is given by

$\text{    }P\left( E\left| F \right. \right)=\frac{P\left( E\cap F \right)}{P\left( F \right)}$

\[\Rightarrow P\left( E\left| F \right. \right)=\frac{\frac{6}{8}}{\frac{7}{8}}\]

Thus \[P\left( E\left| F \right. \right)=\frac{6}{7}\]


7. Two coins are tossed once, where

(i) E: tail appears on one coin, F: one coin shows head       

Ans: Sample Space is given by

$S=\left\{ \left. HH, HT, TH, TT \right\} \right.$

The events E and F and their probabilities are given by

$E=\left\{ \left. HT,TH \right\} \right.$

Therefore $P\left( E \right)=\frac{2}{4}$

$F=\left\{ \left. HT,TH \right\} \right.$

Therefore $P\left( F \right)=\frac{2}{4}$

Also $E\cap F$ is given by

$E\cap F=\left\{ \left. HT,TH \right\} \right.$

We know that $P\left( E\left| F \right. \right)$ is given by

\[\text{    }P\left( E\left| F \right. \right)=\frac{P\left( E\cap F \right)}{P\left( F \right)}\]

$\Rightarrow P\left( E\left| F \right. \right)=\frac{\frac{2}{4}}{\frac{2}{4}}$

Thus we found that $P\left( E\left| F \right. \right)=1$

That is, $\left( E\left| F \right. \right)$ is a sure event

(ii) E: not tail appears, F: no head appears 

Ans: Sample Space is given by

$S=\left\{ \left. HH, HT, TH, TT \right\} \right.$

The events E and F and their probabilities are given by

$E=\left\{ \left. HH \right\} \right.$

Therefore $P\left( E \right)=\frac{1}{4}$

$F=\left\{ \left. TT \right\} \right.$

Therefore $P\left( F \right)=\frac{1}{4}$

Therefore $E\cap F$ is given by

$E\cap F=\phi $

We know that $P\left( E\left| F \right. \right)$ is given by

\[\text{    }P\left( E\left| F \right. \right)=\frac{P\left( E\cap F \right)}{P\left( F \right)}\]

$\Rightarrow P\left( E\left| F \right. \right)=\frac{0}{\frac{1}{4}}$

Thus we found that $P\left( E\left| F \right. \right)=0$


8. A die is thrown three times,

E: 4 appears on the third toss, F: 6 and 5 appear respectively on the first two tosses

Ans: Number of elements in Sample space is given by $216$

The events E and F and their probabilities are given by

$E=\left\{ \left. \begin{align} & \left( 1,1,4 \right),\left( 1,2,4 \right)......\left( 1,6,4 \right) \\ & \left( 2,1,4 \right),\left( 2,2,4 \right)........\left( 2,6,4 \right) \\ & \left( 3,1,4 \right),\left( 3,2,4 \right)..........\left( 3,6,4 \right) \\ & \left( 4,1,4 \right),\left( 4,2,4 \right)..........\left( 4,6,4 \right) \\ & \left( 5,1,4 \right),\left( 5,2,4 \right)...........\left( 5,6,4 \right) \\ & \left( 6,1,4 \right),\left( 6,2,4 \right)...........\left( 6,6,4 \right) \\ \end{align} \right\} \right.$

$F=\left\{ \left. \left( 6,5,1 \right),\left( 6,5,2 \right),\left( 6,5,3 \right),\left( 6,5,4 \right),\left( 6,5,5 \right),\left( 6,5,6 \right) \right\} \right.$

Therefore $P\left( F \right)=\frac{6}{216}$

And hence $E\cap F=\left\{ \left. \left( 6,5,4 \right) \right\} \right.$

Therefore $P\left( E\cap F \right)=\frac{1}{216}$

We know that $P\left( E\left| F \right. \right)$ is given by

$\text{    }P\left( E\left| F \right. \right)=\frac{P\left( E\cap F \right)}{P\left( F \right)}$

$\Rightarrow P\left( E\left| F \right. \right)=\frac{\frac{1}{216}}{\frac{6}{216}}$

Thus $P\left( E\left| F \right. \right)=\frac{1}{6}$


9. Mother, father and son line up at random for a family picture

E: son on one end, F: father in middle

Ans: Let mother (M), father (F), and son (S) line up for the family picture, then the sample space will be as shown

$A=\left( \left. MFS,MSF,FMS,FSM,SMF,SFM \right\} \right.$

The events E and F and their probabilities are 

$E=\left\{ \left. MFS, FMS, SMF, SFM \right\} \right.$

$F=\left\{ \left. MFS,SFM \right\} \right.$

Therefore $P\left( F \right)=\frac{2}{6}$

Hence $E\cap F=\left\{ \left. MFS,SFM \right\} \right.$

Therefore $P\left( E\cap F \right)=\frac{2}{6}$

We know that $P\left( E\left| F \right. \right)$ is given by

$\Rightarrow P\left( E\left| F \right. \right)=\frac{P\left( E\cap F \right)}{P\left( F \right)}$

$\Rightarrow P\left( E\left| F \right. \right)=\frac{\frac{4}{6}}{\frac{4}{6}}$

Thus $P\left( E\left| F \right. \right)=1$


10. A black and a red dice are rolled.

  1. Find the conditional probability of obtaining a sum greater than $9$, given that the black die resulted in a $5$.

Ans: Let the first observation come from the black die and the second from the red die respectively 

In the case when two dices are rolled the elements in sample space is $36$

The events A and B and their probabilities are given by

\[A=\left\{ \left( 4,6 \right),\left( 5,5 \right),\left( 5,6 \right),\left( 6,4 \right),\left( 6,5 \right),\left( 4,6 \right)\left( 6,6 \right) \right\}\]

Where A is the event when the sum is greater than $9$

Similarly, 

\[B=\left\{ \left( 5,1 \right),\left( 5,2 \right),\left( 5,3 \right),\left( 5,4 \right),\left( 5,5 \right),\left( 5,6 \right) \right\}\]

Where B is the event when black die resulted in a $5$

Therefore $P\left( B \right)=\frac{6}{36}$

And hence $A\cap B=\left\{ \left( 5,5 \right),\left( 5,6 \right) \right\}$

Therefore the conditional probability of obtaining a sum greater than $9$, given that the black die resulted in a $5$$P\left( A\left| B \right. \right)$ is given by

$\Rightarrow P\left( A\left| B \right. \right)=\frac{P\left( A\cap B \right)}{P\left( B \right)}$

$\Rightarrow P\left( A\left| B \right. \right)=\frac{\frac{2}{36}}{\frac{6}{36}}$

Thus $P\left( A\left| B \right. \right)=\frac{1}{3}$

  1. Find the conditional probability of obtaining the sum 8, given that the red die resulted in a number less than 4.

Ans: Let E and F be the events and their probabilities defined as 

E: Sum of the observations is $8$

$E=\left\{ \left( 2,6 \right),\left( 3,5 \right),\left( 4,4 \right),\left( 5,3 \right),\left( 6,2 \right) \right\}$

F: red die resulted in a number less than $4$

\[F=\left\{ \begin{align} & \left( 1,1 \right),\left( 1,2 \right),\left( 1,3 \right),\left( 2,1 \right),\left( 2,2 \right),\left( 2,2 \right) \\ & \left( 3,1 \right),\left( 3,2 \right),\left( 3,3 \right),\left( 4,1 \right),\left( 4,2 \right),\left( 4,3 \right) \\ & \left( 5,1 \right),\left( 5,2 \right),\left( 5,3 \right),\left( 6,1 \right),\left( 6,2 \right),\left( 6,3 \right) \\ \end{align} \right\}\]

Therefore $P\left( F \right)=\frac{18}{36}$

And hence $E\cap F=\left\{ \left( 5,3 \right),\left( 6,2 \right) \right\}$

Therefore $P\left( E\cap F \right)=\frac{2}{36}$

The conditional probability of obtaining the sum $8$, given that the red die resulted in a number less than $4$is given $P\left( E\left| F \right. \right)$ as shown

$P\left( E\left| F \right. \right)=\frac{P\left( E\cap F \right)}{p\left( F \right)}$

$\Rightarrow P\left( E\left| F \right. \right)=\frac{\frac{2}{36}}{\frac{18}{36}}$

Thus $P\left( E\left| F \right. \right)=\frac{1}{9}$


11. A fair die is rolled. Consider events E = {1, 3, 5), F = {2, 3} and G = {2, 3, 4, 5}. Find 

(i) $\mathbf{P}\left( E\left| F \right. \right)$ and $\mathbf{P}\left( F\left| E \right. \right)$

Ans: The sample space is given by

$S=\left\{ 1,2,3,4,5,6 \right\}$

It is given in the question 

$E=\left\{ 1,3,5 \right\}$

$F=\left\{ 2,3 \right\}$

Therefore $E\cap F=\left\{ 3 \right\}$

Hence $P\left( E\cap F \right)=\frac{1}{6}$

$\therefore P\left( E \right)=\frac{3}{6}$

$\therefore P\left( F \right)=\frac{2}{6}$

Hence $P\left( E\left| F \right. \right)$ is given by 

$P\left( E\left| F \right. \right)=\frac{P\left( E\cap F \right)}{P\left( F \right)}$

$\Rightarrow P\left( E\left| F \right. \right)=\frac{\frac{1}{6}}{\frac{2}{6}}$

Similarly $P\left( F\left| E \right. \right)$ si given by

$P\left( F\left| E \right. \right)=\frac{\frac{1}{6}}{\frac{3}{6}}$

Thus $P\left( E\left| F \right. \right)=\frac{1}{2}$ and $P\left( F\left| E \right. \right)=\frac{1}{3}$

(ii). $\mathbf{P}\left( E\left| G \right. \right)$ and $\mathbf{P}\left( G\left| E \right. \right)$

It is given in the question 

$E=\left\{ 1,3,5 \right\}$

$\therefore P\left( E \right)=\frac{3}{6}$

$G=\left\{ 2,3,4,5 \right\}$

$\therefore P\left( G \right)=\frac{4}{6}$

$\therefore E\cap G=\left\{ 3,5 \right\}$

$\therefore P\left( E\cap G \right)=\frac{2}{6}$

Therefore $P\left( E\left| G \right. \right)=\frac{\left( E\cap G \right)}{P\left( G \right)}$

$\Rightarrow P\left( E\left| G \right. \right)=\frac{\frac{2}{6}}{\frac{4}{6}}$

Thus $P\left( E\left| G \right. \right)=\frac{1}{2}$

Similarly

$P\left( G\left| E \right. \right)=\frac{\frac{2}{6}}{\frac{3}{6}}$

Thus $P\left( G\left| E \right. \right)=\frac{2}{3}$

Therefore, $P\left( E\left| G \right. \right)=\frac{1}{2}$ and $P\left( G\left| E \right. \right)=\frac{2}{3}$

(iii) $\mathbf{P}\left( \left( E\bigcup F \right)\left| G \right. \right)$ and $\mathbf{P}\left( \left( E\cap F \right)\left| G \right. \right)$

Ans: The sample space is given by

$S=\left\{ 1,2,3,4,5,6 \right\}$

$G=\left\{ 2,3,4,5 \right\}$

We have $E\bigcup F=\left\{ 1,2,3,5 \right\}$

Therefore \[\begin{align} & \left( E\bigcup F \right)\cap G=\left\{ 1,2,3,5 \right\}\cap \left\{ 2,3,4,5 \right\} \\ & \text{                    =}\left\{ 2,3,5 \right\} \\ \end{align}\]

Also \[\begin{align} & \left( E\cap F \right)\cap G=\left\{ 1,2,3,5 \right\}\cap \left\{ 3 \right\} \\ & \text{                    =}\left\{ 3 \right\}\text{ } \\ \end{align}\]

$\therefore P\left( E\bigcup F \right)\cap G=\frac{3}{6}$

$\therefore P\left( E\cap F \right)\cap G=\frac{1}{6}$

$\therefore P\left( E\bigcup F\left| G \right. \right)=\frac{P\left( E\bigcup F \right)\cap G}{P\left( G \right)}$

$\Rightarrow P\left( E\bigcup F\left| G \right. \right)=\frac{\frac{3}{6}}{\frac{4}{6}}$

Thus $P\left( E\bigcup F\left| G \right. \right)=\frac{3}{4}$

Similarly 

$P\left( E\cap F\left| G \right. \right)=\frac{\frac{1}{6}}{\frac{4}{6}}$

Thus, $P\left( E\bigcup F\left| G \right. \right)=\frac{3}{4}$ and $P\left( E\cap F\left| G \right. \right)=\frac{1}{4}$


12. Assume that each born child is equally like to be a boy or a girl. If a family has two children, what is the conditional probability that both are girls given that 

i. The youngest is a girl

Ans: The sample space for a family having two children is given by

$S=\left\{ \left( B,B \right),\left( B,G \right),\left( G,G \right),\left( G,B \right) \right\}$

Where B refers to boy child and G refers to girl child 

Let an event be defined as 

E: Both children are girls 

$E=\left\{ \left( GG \right) \right\}$

$\therefore P\left( E \right)=\frac{2}{4}$

Let F be an event defined as

F: youngest child is girl

$F=\left\{ \left( BG \right),\left( GG \right) \right\}$

$\therefore E\cap F=\left\{ \left( GG \right) \right\}$

$\therefore P\left( E\cap F \right)=\frac{1}{4}$

We know that $P\left( E\left| F \right. \right)$ is given by

 $P\left( E\left| F \right. \right)=\frac{P\left( E\cap F \right)}{P\left( F \right)}$

$\Rightarrow P\left( E\left| F \right. \right)=\frac{\frac{1}{4}}{\frac{2}{4}}$

Thus $P\left( E\left| F \right. \right)=\frac{1}{2}$

ii. At least one is a girl

Ans: The sample space for a family having two children is given by

$S=\left\{ \left( B,B \right),\left( B,G \right),\left( G,G \right),\left( G,B \right) \right\}$

Where B refers to boy child and G refers to girl child 

Let an event be defined as 

E: Both children are girls 

$E=\left\{ \left( GG \right) \right\}$

$\therefore P\left( E \right)=\frac{2}{4}$

Let A be an event defined as

$\text{         }A=\left\{ \left( B,G \right),\left( G,B \right),\left( G,G \right) \right\}$

$\therefore P\left( A \right)=\frac{3}{4}$

$\therefore E\cap A=\left\{ \left( G,G \right) \right\}$

$\therefore P\left( E\cap A \right)=\frac{1}{4}$

We know that 

$P\left( E\left| A \right. \right)$ is given by

$P\left( E\left| A \right. \right)=\frac{P\left( \cap A \right)}{P\left( A \right)}$

$\Rightarrow P\left( E\left| A \right. \right)=\frac{\frac{1}{4}}{\frac{3}{4}}$

Thus $P\left( E\left| A \right. \right)=\frac{1}{3}$


13. An instructor has a  bank consisting of 300 easy True/False s, 200 difficult True/False s, 500 easy multiple choice questions and 400 difficult multiple choice questions. If a  is selected at random from the  bank, what is the probability that it will be an easy  given that it is a multiple choice ?

Ans: The given data can be represented as



True/False

Multiple choice

Total

Easy

300

500

800

Difficult

200

400

600

Total

500

900

1400


Let us have following notations 

E for easy questions, M for multiple questions, D for difficult questions and T for true/false questions

It is given that total number of questions is $1400$, that of multiple questions is $900$

Hence probability for selecting easy multiple choice questions is given by

$P\left( E\cap M \right)=\frac{5}{14}$ (since $\frac{500}{1400}=\frac{5}{14}$)

Probability for selecting multiple choice questions is given by

$P\left( M \right)=\frac{9}{14}$ (since $\frac{90}{1400}=\frac{9}{14}$)

$\therefore P\left( E\left| M \right. \right)=\frac{P\left( \cap M \right)}{P\left( M \right)}$

$\Rightarrow P\left( E\left| M \right. \right)=\frac{\frac{5}{14}}{\frac{9}{14}}$

Thus $P\left( E\left| M \right. \right)=\frac{5}{9}$


14. Given that the two numbers appearing on throwing the two dice are different. Find the probability of the event ‘the sum of numbers on the dice is $4$’.

Ans: Let A and be events defined as 

A:the sum of the numbers on the dice is $4$ 

B:the two numbers appearing on throwing the two dice are different. 

$\therefore A=\left\{ \left( 1,3 \right),\left( 2,3 \right),\left( 3,1 \right) \right\}$

$\therefore B=\left\{ \begin{align} & \left( 1,2 \right),\left( 1,3 \right)....\left( 1,6 \right) \\ & \left( 2,1 \right),\left( 2,2 \right)....\left( 2,6 \right) \\ & \left( 3,1 \right),\left( 3,2 \right)......\left( 3,6 \right) \\ & \left( 4,1 \right),\left( 4,2 \right).......\left( 4,6 \right) \\ & \left( 5,1 \right),\left( 5,2 \right).........\left( 5,6 \right) \\ & \left( 6,1 \right),\left( 6,2 \right).........\left( 6,6 \right) \\ \end{align} \right\}$

Therefore $P\left( B \right)=\frac{30}{36}$

$\therefore A\cap B=\left\{ \left( 1,3 \right),\left( 3,1 \right) \right\}$

$P\left( A\cap B \right)=\frac{2}{36}$

We know that $P\left( A\left| B \right. \right)$ is given by

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

$\Rightarrow P\left( A\left| B \right. \right)=\frac{\frac{2}{36}}{\frac{30}{36}}$

Thus \[P\left( A\left| B \right. \right)=\frac{1}{15}\]

 

15. Consider the experiment of throwing a die, if a multiple of $3$ comes up, throw the die again and if any other number comes, toss a coin. Find the conditional probability of the event ‘the coin shows a tail’, given that ‘at least one die shows as $3$’.

Ans: For this case the sample space is given by  

$S=\left\{ \begin{align} & \left( 1,H \right),\left( 2,H \right),\left( 1,T \right),\left( 1,T \right),\left( 3,1 \right),\left( 3,2 \right),\left( 3,3 \right),\left( 3,4 \right),\left( 3,5 \right),\left( 3,6 \right) \\ & \left( 4,H \right),\left( 4,T \right),\left( 5,H \right),\left( 5,T \right),\left( 6,1 \right),\left( 6,2 \right)......\left( 6,6 \right) \\ \end{align} \right\}$

Let A and B be events defined as 

A: The coin shows tail

B: at least one die shows $3$

$\therefore A=\left\{ \left( 1,T \right),\left( 2,T \right),\left( 4,T \right),\left( 5,T \right) \right\}$

$\therefore B=\left\{ \left( 3,1 \right),\left( 3,2 \right),\left( 3,3 \right),\left( 3,4 \right),\left( 3,5 \right),\left( 3,6 \right),\left( 6,3 \right) \right\}$

$\therefore P\left( B \right)=\frac{7}{36}$

Also it is observable that $A\cap B=\phi $

We know that $P\left( A\left| B \right. \right)$ given by

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

$P\left( A\left| B \right. \right)=\frac{0}{\frac{7}{36}}$

Thus $P\left( A\left| B \right. \right)=0$


16. If $P\left( A \right)=\frac{1}{2}$and $P\left( B \right)=0$then $P\left( A\left| B \right. \right)$is

  1.  $0$

  2.  $\frac{1}{2}$

  3.  Not Defined

  4.  $1$

Ans: It is given that $P\left( A \right)=\frac{1}{2}$ and $P\left( B \right)=0$

We know that $P\left( A\left| B \right. \right)$ is given by

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

$\Rightarrow P\left( A\left| B \right. \right)=\frac{P\left( \cap B \right)}{0}$ 

Thus $P\left( A\left| B \right. \right)$ is not defined


17. If A and B are events such that $P\left( A\left| B \right. \right)=P\left( B\left| A \right. \right)$ then

(A)$A\subset but\text{ A}\ne \text{B}$ (B) $A=B$ (C)$A\cap B=\phi $) (D)= $P\left( A \right)=P\left( B \right)$

Ans: It is given in the question that  

$\text{       }P\left( A\left| B \right. \right)=P\left( B\left| A \right. \right)$

$\Rightarrow \frac{P\left( A\cap B \right)}{P\left( B \right)}=\frac{P\left( A\cap B \right)}{P\left( A \right)}$

$\text{     }\Rightarrow \frac{P\left( A \right)}{1}=\frac{P\left( B \right)}{1}$

Thus we found that $P\left( A \right)=P\left( B \right)$


Conclusion

NCERT Solutions for Maths Exercise 13.1 Class 12 Chapter 13 - Probability by Vedantu gives clear and simple explanations. Focus on learning the formulas for conditional probability and the multiplication rule. These are key for solving complex problems accurately. By practicing these exercises, students can improve their problem-solving skills and get a strong grasp of probability, which will help them do better in exams.


Class 12 Maths Chapter 13: Exercises Breakdown

S.No.

Chapter 13 - Probability Exercises in PDF Format

1

Class 12 Maths Chapter 13 Exercise 13.2 - 18 Questions & Solutions

2

Class 12 Maths Chapter 13 Exercise 13.3 - 14 Questions & Solutions

3

Class 12 Maths Chapter 13 Miscellaneous Exercise - 17 Questions & Solutions



CBSE Class 12 Maths Chapter 13 Other Study Materials



NCERT Solutions for Class 12 Maths | Chapter-wise List

Given below are the chapter-wise NCERT 12 Maths solutions PDF. Using these chapter-wise class 12th maths ncert solutions, you can get clear understanding of the concepts from all chapters.




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FAQs on CBSE Class 12 Mathematics Solutions for Chapter 13 Probability – NCERT Exercise 13.1 [2025-26]

1. How should you approach solving conditional probability problems according to the NCERT Solutions for Class 12 Maths Chapter 13?

For solving conditional probability problems, follow these steps:

  • Identify the required events (E and F) from the problem statement.
  • List all possible outcomes in the sample space.
  • Find which outcomes correspond to event F (the condition) and the intersection E ∩ F.
  • Calculate P(F) and P(E ∩ F).
  • Apply the formula: P(E|F) = P(E ∩ F) / P(F), ensuring P(F) ≠ 0.
This structured approach as per CBSE 2025-26 maximises accuracy in board exams.

2. What areas are covered in the NCERT Class 12 Maths Chapter 13 Probability solutions?

The solutions cover the complete syllabus, including:

  • Exercise 13.1: Conditional Probability; formula application
  • Exercise 13.2: Multiplication Theorem and Independent Events
  • Exercise 13.3: Total Probability Law and Bayes' Theorem
  • Exercise 13.4: Random Variables and Probability Distributions
  • Exercise 13.5: Bernoulli Trials and Binomial Distributions
All concepts are aligned with the latest CBSE Class 12 syllabus for 2025–26.

3. How does the NCERT solution explain the use and significance of the multiplication theorem in probability?

The multiplication theorem is used to find the probability of two events happening together. It states: P(E ∩ F) = P(E) × P(F|E). When E and F are independent, it simplifies to P(E ∩ F) = P(E) × P(F). The solutions make you practise this with sequential events, typical of CBSE exam questions.

4. What is Bayes' Theorem and how is it applied in Class 12 NCERT probability problems?

Bayes' Theorem helps compute the reverse probability, i.e., the likelihood that a particular cause was responsible for a known effect. The steps followed are:

  • Define a partition of the sample space (mutually exclusive and exhaustive events).
  • List given probabilities for each event and for the occurrence of the effect given each event.
  • Apply the Bayes' formula: P(E₁|A) = [P(E₁) × P(A|E₁)] / Σ[P(Eᵢ) × P(A|Eᵢ)].
This is especially important in questions involving reverse probabilities and partitions.

5. What strategy is recommended by the NCERT solutions for constructing a probability distribution of a random variable?

The standard method involves:

  • Define the random variable and its possible values (e.g., number of heads in coin tosses).
  • Calculate the probability for each value.
  • Arranging these in a table showing the values of X and their corresponding probabilities.
The sum of all probabilities should equal 1. This systematic approach is used in Class 12 exams.

6. Why are P(A|B) and P(B|A) generally not equal in probability problems?

P(A|B) and P(B|A) are not generally equal because each represents the probability of different conditions.

  • P(A|B): Probability of A occurring when B has occurred (P(A ∩ B) / P(B)).
  • P(B|A): Probability of B occurring when A has occurred (P(A ∩ B) / P(A)).
The denominators are different unless P(A) = P(B). NCERT solutions demonstrate this through worked examples, reinforcing that the 'given' part significantly affects calculations.

7. What are common misconceptions in defining events for Bayes' Theorem obtained from NCERT Exercise 13.3?

A common mistake is failing to define events that are both mutually exclusive and exhaustive. For correct application, the initial events (E₁, E₂, ...) must not overlap and should cover all possibilities in the sample space. Overlapping or incomplete partitions lead to incorrect answers. The NCERT solutions highlight the importance of proper event definition when applying Bayes' theorem.

8. How does the property of ‘independent events’ simplify the multiplication rule in probability as explained in the NCERT Solutions?

If two events are independent, the occurrence of one does not affect the probability of the other. This simplifies the rule from P(A ∩ B) = P(A) × P(B|A) to P(A ∩ B) = P(A) × P(B). The NCERT solutions use this in problems involving multiple coin tosses or the rolling of separate dice, which are classic independent scenarios in board exams.

9. Under what conditions can the binomial distribution formula be used in Chapter 13 as per CBSE guidelines?

The binomial distribution can be applied when:

  • The number of trials (n) is fixed.
  • Each trial is independent.
  • Each trial results in just two possible outcomes (success or failure).
  • The probability of success (p) is constant for all trials.
The NCERT solutions for Exercise 13.5 focus on verifying these before applying the formula for P(X = r).

10. How can students avoid common mistakes when presenting solutions to probability problems in board exams?

To avoid losing marks, students should:

  • Clearly define all events before starting calculations.
  • Show all steps (listing the sample space, events, intersections, and final calculation).
  • Check that probabilities are within [0, 1].
  • Use correct formulas and state assumptions (like independence).
Presenting solutions stepwise, as shown in official NCERT Solutions for Chapter 13, ensures maximum clarity and marks in CBSE board exams.

11. What should you do if the denominator in a conditional probability formula (like P(B) in P(A|B)) becomes zero?

If P(B) = 0 in the conditional probability formula P(A|B) = P(A ∩ B) / P(B), the expression is not defined. According to NCERT guidance, this is because you cannot condition on an impossible event.

12. How do the NCERT Solutions for Class 12 Maths Chapter 13 help prepare for CBSE board exams?

The stepwise NCERT solutions for Chapter 13 ensure complete coverage of all concepts, formulas, and problem types expected in the CBSE exams 2025–26. Practising from these solutions helps develop a clear thought process, proper presentation, and confidence in tackling a wide variety of probability questions according to board marking schemes.

13. Why must events in Bayes’ Theorem be mutually exclusive and exhaustive as stressed in NCERT solutions?

Bayes’ theorem requires events to be mutually exclusive (no overlap) and exhaustive (all possible outcomes). This ensures that the sample space is fully and uniquely partitioned, allowing correct calculation of total probability and conditional probabilities for reverse probability scenarios.

14. How does understanding conditional probability support success in later exercises involving Bernoulli Trials and Binomial Distributions?

Conditional probability lays the groundwork for understanding Bernoulli Trials and Binomial Distributions, as it enables the calculation of probabilities for composite or dependent events. Later exercises build on this foundation, applying these concepts to more complex, structured scenarios as in NCERT Exercise 13.5.