Page 330 - DECO504_STATISTICAL_METHODS_IN_ECONOMICS_ENGLISH

Unit 26: Theory of Probability: Introduction and Uses
Dilfraz Singh, Lovely Professional University

Unit 26: Theory of Probability: Introduction and Uses Notes

CONTENTS
Objectives
Introduction
26.1 Introduction to Theory of Probability
26.2 Uses of Theory of Probability
26.3 Summary

26.4 Key-Words
26.5 Review Questions
26.6 Further Reading

Objectives

After reading this unit students will be able to:
• Introduce Theory of Probability.
• Discuss the Uses of Theory of Probability.
Introduction

In day-to-day life, we all make use of the word ‘probability’. But generally people have no definite
idea about the meaning of probability. For example, we often hear or talk phrases like, “Probability
it may rain today”; “It is likely that the particular teacher may not come for taking his class today”;
“there is a chance that the particular student may stand first in the university examination”; “it is
possible that the particular company may get the contract which it bid last week”; “most probably I
shall be returning within a week”; “it is possible that he may not be able to join his duty”. In all the
above statements, the terms—possible, probably, likely, chance, etc., convey the same meaning, i.e.,
the events are not certain to take place. In other words, there is involved an element of uncertainty or
chance in all these cases. A numerical measure of uncertainty is provided by the theory of probability.
The aim of the probability theory is to provide a measure of uncertainty. The theory of probability
owes its origin to the study of games of chance like games of cards, tossing coins, dice, etc. But in
modern times, it has great importance is decision making problems.

26.1 Introduction to Theory of Probability

We have understood the difference between descriptive and inferential statistics. The study of
probability provides a basis for inferential statistics. Inferential statistics involves sample selection,
computing sample statistic on the basis of the concerned sample, and then inferring population
parameter on the basis of the sample statistic. We do this exercise because population parameter is
unknown. We try to estimate the unknown population parameter on the basis of the known sample
statistic. This procedure works on uncertainty. By applying some defined statistical rules and
procedures, an analyst can assign the probability of obtaining a result. To make rational decisions, a
decision maker must have a deep understanding of probability theory. This understanding enhances
his capacity to make optimum decisions in an uncertain environment. This unit focuses on the basic
concept of probability which will serve as the foundation of probability distributions. A sound
knowledge of probability and probabilistic distributions also helps in developing probabilistic decision
models.

LOVELY PROFESSIONAL UNIVERSITY 325