# Probability Problems, Shortcut Tricks and Examples

Probability Examples - Page 2
Probability Questions - Page 3
Probability Lectures - Page 4

Probability In an experiment if ‘ n ‘ is the number of exhaustive cases and ‘m’ is the number of favourable cases of an event A. Then the probability of event A is denoted by P(A).

P(A) = $\frac{Number\; of\; Favourable\; Cases}{Number \; of \; Exhaustive\; Cases\; }\: =\:\;\frac{m}{n}\: =\,: \frac{n(A)}{n(S)}$

## Probability Theory

• Random Experiment : An experiment in which all possible out comes are known and the exact output cannot be predicated in advance is called Random Experiment.
EXAMPLE :
(i) Tossing a fair coin
(ii) Rolling an unbiased dice
(iii) Drawing a card from a pack of well shuffled cards
• Trail : Conducting a Random Experiment once is known as a Trail
• Outcome : The result of a Trail in the random experiment is called as Outcome
EXAMPLE : In tossing a single coin outcomes are H and T
• Sample Space : A set of all possible outcomes of a random experiments is known as Sample Space
EXAMPLE :
( i ) In the experiment of tossing a coin the sample space S H,T
( ii ) If Two coins are tossed then S HH,HT,TH,TT
( iii ) In throwing a die S 1,2,3,4,5,6
• Event : Any non-empty subset of a Sample Space is called an Event
EXAMPLE :
( i ). in Tossing a single coin getting Head or Tail is an Event
( ii )Getting an Ace (or) Diamond from a pack of 52 cards is an event
• Exhaustive Events : The total number of possible outcomes of an experiment is known as Exhaustive Events
EXAMPLE : In the experiment of throwing a die the total number of possible outcomes = 6
• Favourable Events : The number of event which favour the happing of the events are known as Favourable Cases (or) Events
EXAMPLE : In tossing two dice the number of cases favourable to getting the sum 3 is (2, 1), (1, 2) i.e., 2
• Mutually Exclusive Events : If two events have no common outcomes then they are called Mutually Exclusive
EXAMPLE: In tossing a coin the events Head & Tail are mutually exclusive because Head & Tail cannot happen at the same time.
• Independent Events : Two events are said to be independent if the happening of one event does not affect the happening of the other.
• Dependent Events : Two events are said to be dependent if the happening of an event will affect the happening of the other event .
EXAMPLE: If we draw a card from a pack of 52 cards and replace it before we draw a second card. The second card is independent of first one. If we don’t replace the first card before the second draw. The second draw depends on the first one.
• Axioms of Probability : Let S be the sample space and A be the event i.e., $A\: \sqsubseteq \: S$
1. $0\: \leq \: P(A)\: \leq \: 1\: ,\forall \: A\: \sqsubseteq \: S$
2. P(S) = 1
3. If A and B are mutually exclusive ( Disjoint i.e., $A\: \cap \: B\: =\: \Phi$ )
$P(A\cup B)$ = P(A)+P(B)
• Results of Probability :
1
. for any two events A & B , $P(A\cup B)\ =P(A)+P(B)-P(A\cap B)$
2. If $\bar{A}$ denotes compliment of event A then , $P\bar{A}$ = 1-P(A)
3. For any two events A & B , $P(\bar{A}\cap B)=P(B)-P(A\cap B)$
4. For any three events A, B & C , $(A\cup B\cup C)=P(A)+P(B)+P(C)-P(A\cap B)-P(B\cap C)-P(A\cap C)+P(A\cap B\cap C)$

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