What is Probability

What is Probability?

—  Probability is the measure of how likely an event is.

   “ the ratio of the number of favourable cases to the number of all the cases “ or

         P(E) = Number of  outcomes favourable to E

                     Number of all possible outcomes of the experiment

—  PROBABILITY is a measure of the degree to which an occurrence is certain [or uncertain].

Concept of Probability       

v  The concept of Probability originated in the 16^th century when an Italian physician and mathematician J.Cardan wrote the first book on the subject , The Book on Games of Chance. Since its inception, the study of probability has attracted the attention of great mathematicians such as James Bernoulli (1654-1705) ,

v Probability theory is a very fascinating subject which can be studied at various mathematical levels. Probability is the foundation of statistical theory and applications.

v Several mathematicians like Pascal, James Bernoulli, De-Moivre, Bayes applied the theory of permutations and combinations to quantify or calculate probability. Today the probability theory has become one of the fundamental technique in the development of Statistics.

v The term “probability” in Statistics refers to the chances of occurrence of an event among a large number of possibilities.

GAMBLER’S FALLACY

—  The gambler's fallacy, also known as the Monte Carlo fallacy , is the mistaken belief that if something happens more frequently than normal during some period, then it will happen less frequently in the future (presumably as a means of balancing nature).For  eg : A coin is tossed 5 times, and all the results comes as ‘ HEADS ’. So during the 6^th attempt , the person may feel that A TAIL IS DUE  and hence falls prey to the Fallacy by predicting a Tail ,not thinking that the PROBABILITY OF OBTAINING EITHER HEADS OR TAILS REMAINS SAME

—  The use of the term Monte Carlo fallacy originates from the most famous example of this phenomenon, which occurred in a Monte Carlo Casino on 18/8/1913.Then,a ball fell in black  15 TIMES . Because of such an occuring, gambler’s started betting on RED.But they fell prey to the Fallacy as RED TURNED-UP after 26 attempts.

APPLICATION OF PROBABILITY

—  Probability theory is applied in day to day life in risk assessments and in trade on financial markets

—  Another Significant application of probability theory in everyday life is reliability. Many consumer products, such as automobiles and consumer electronics use reliability theory in product design to reduce the probability of failure.

TERMINOLOGIES

Random Experiment:

—  If an experiment or trial is repeated under the same conditions for any number of times and it is possible to count the total number of outcomes is called as “Random Experiment”.

Sample Space:

—  The set of all possible outcomes of a random experiment is known as “Sample Space” and denoted by set S. [this is similar to Universal set in Set Theory] The outcomes of the random experiment are called sample points or outcomes.

Exhaustive Events:

—  The total number of all possible elementary outcomes in a random experiment is known as ‘exhaustive events’. In other words, a set is said to be exhaustive, when no other possibilities exists.

 Favorable Events:

—  The elementary outcomes which entail or favor the happening of an event is known as ‘favorable events’ i.e., the outcomes which help in the occurrence of that event.

Equally likely or Equi-probable Events:

—  Outcomes are said to be ‘equally likely’ if there is no reason to expect one outcome to occur in preference to another. i.e., among all exhaustive outcomes, each of them has equal chance of occurrence.

 Complementary Events:

—  Let E denote occurrence of event. The complement of E denotes the non occurrence of event E. Complement of E is denoted by ‘Ē’.

          Independent Events:    

—  Two or more events are said to be ‘independent’, in a series of a trials if the outcome of one event is does not affect the outcome of the other event or vise versa.

—  In other words, several events are said to be ‘dependents’ if the occurrence of an event is affected by the occurrence of any number of remaining events, in a series of trials.

 Mutually Exclusive Events:

—  Events are said to be ‘mutually exclusive’ if the occurrence of an event totally prevents occurrence of all other events in a trial. In other words, two events A and B cannot occur simultaneously.

THREE TYPES OF PROBABILITY

1.     Theoretical probability:

 For theoretical reasons, we assume that all n possible outcomes of a particular experiment are equally likely, and we assign a probability of  to each possible outcome. 

2.     Relative frequency

Relative Frequency is based on observation or actual measurements.

3.     Personal or subjective probability:

          These are values (between 0 and 1 or 0 and 100%) assigned by individuals based on how likely they think events are to occur. 

MEASUREMENT OF PROBABILITY

          There are three approaches to construct a measure of probability of occurrence of an event. They are:

—   Classical or Mathematical Approach:

                             In this approach we assume that an experiment or trial results in any one of many possible outcomes, each outcome being Equi-probable or equally-likely.

Definition: If a trial results in ‘n’ exhaustive, mutually exclusive, equally likely and independent outcomes, and if ‘m’ of them are favorable for the happening of the event E, then the probability ‘P’ of occurrence of the event ‘E’ is given by-

—  Empirical or Statistical Approach:

                   This approach is also called the ‘frequency’ approach to probability. Here the probability is obtained by actually performing the experiment large number of times. As the number of trials n increases, we get more accurate result.

          Definition: Consider a random experiment which is repeated large number of times under essentially homogeneous and identical conditions.
—  Axiomatic Approach:

This approach was proposed by Russian Mathematician A.N.Kolmogorov in1933.

‘Axioms’ are statements which are reasonably true and are accepted as such, without seeking any proof.

Definition: Let S be the sample space associated with a random experiment. Let A be any event in S. then P(A) is the probability of occurrence of A if the following axioms are satisfied.