Research Methodology Chapter 8.1

In the field of statistics, probability distribution refers to the mathematical function that describes the likelihood of different outcomes occurring in a particular event or experiment. A probability distribution is a function that describes the likelihood of different outcomes in a random event. It provides a way to assign probabilities to each possible outcome of a random event, based on the likelihood of that outcome occurring. 

There are several types of probability distributions, each with its own characteristics and applications. 

Discrete probability distributions: These are used when the possible outcomes of a random event are discrete, or separate and distinct, rather than continuous. Examples include the roll of a die, the flip of a coin, and the outcome of a lottery draw. Common discrete probability distributions include the Binomial distribution, the Geometric distribution, and the Poisson distribution.

Continuous probability distributions: These are used when the possible outcomes of a random event are continuous, or can take on any value within a certain range. Examples include the height of a person, the weight of an object, and the time it takes for an event to occur. Common continuous probability distributions include the Normal distribution, the Uniform distribution, and the Exponential distribution.

Multivariate probability distributions: These are used when there are multiple random variables involved in a single event. Examples include the distribution of height and weight for a group of people, or the distribution of temperatures in different locations. Common multivariate probability distributions include the Multivariate Normal distribution and the Multivariate Uniform distribution.

In the next three chapters we will learn in detail about 1) Normal distribution, 2) Binomial distribution, and 3) Poisson Distribution.