CBSE Class 12 Maths Chapter 13 Probability Formulas List, Important Definitions & Examples

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CBSE 10th Maths Probability Formulas: Check here for all the important formulas of mathematics chapter 13 Probability of Class 12, along with major definitions and examples.

CBSE Class 12 Maths Formulas Probability: Every student knows the Pythagoras theorems and the quadratic equations. This is essential mathematical knowledge that most people possess, whether math enthusiasts or not. Formulas, theorems and other rules help simplify equations and problems and solve them quickly.

Mathematics is a vast science with immense unexplored potential. It’s also a difficult subject to teach. But nowadays, students are introduced to complex topics like calculus as early as grade 9 or 10. These are concepts that renowned mathematicians dedicated their entire lives work to. And it’s all thanks to the new and easy-to-understand methods of learning.

We now have ways to simplify complex mathematical concepts using formulas, theorems, identities, properties and proofs. Some chapters, like Probability, are entirely formula-based, and questions become exponentially more challenging without them.

Probability is a topic students are introduced to at a very early age and is also one of the most intriguing concepts in math. It deals with chance and figuring out the numerical value of the likelihood of events occurring.

Here at Jagran Josh, we cover the list of all important formulas, definitions, and glossaries of probability, along with necessary examples. You can check the CBSE Class 12 Maths Chapter 13 Probability Formulas below.

CBSE Class 12 Maths Chapter 13 Probability MCQs

CBSE Class 12 Maths Chapter 13 Probability Formulas and Theorems

We have listed the important formulas of CBSE Class 12 Probability here.

Conditional Probability: The probability of an event E, given that the event F has already occurred is called conditional probability. It’s denoted by

P(E|F) = P(E ∩ F)/P(F) where P(F) ≠ 0

Properties of Conditional Probability:

There are three main properties of conditional probability:

If E and F be events of a sample space S of an experiment

i) P(S|F) = P(F|F)=1

ii) For any two events A and B of sample space S if F is another event such that P(F) = 0, P ((A U B) |F) =P (A|F)+P (B|F)-P ((A ∩ B)|F)

iii) P(E’|F) = 1 -P(E|F)

Multiplication Theorem on Probability:If E and F are independent, then P (E ∩ F) = P (E) P (F)

P (E|F) = P (E), P (F) ≠ 0

P (F|E) = P (F), P(E) ≠ 0

Theorem of Total Probability: Let {E₁, E₂, …,E_n) be a partition of a sample space and suppose that each of E₁, E₂, …, En has a nonzero probability. Let A be any event associated with S, then P(A) = P(E₁) P (A|E₁) + P (E₂) P (A|E₂) + … + P (E_n) P(A|E_n)
Bayes’ Theorem:If E₁, E₂, …,E_nare events which constitute a partition of sample space S, ie. E₁, E₂, …, Enare pairwise disjoint and E₁U E₂ U…U E_n = S and A be any event with nonzero probability, then