The assumptions of the binomial distribution are as follows:
Fixed Number of Trials:
The binomial distribution assumes a fixed number of independent trials, denoted by ‘n’. Each trial can have only two possible outcomes, often referred to as success (S) or failure (F).
The trials must be independent of each other, meaning that the outcome of one trial does not influence the outcome of another trial. This assumption is important to ensure that each trial has the same probability of success.
Constant Probability of Success:
The probability of success, denoted by ‘p’, must remain constant for each trial. In other words, the probability of success does not change from trial to trial.
Each trial must have only two possible outcomes, typically referred to as success or failure. These outcomes are mutually exclusive, meaning that a trial cannot be both a success and a failure simultaneously.
The distribution deals with discrete variables, meaning that the number of successes out of ‘n’ trials must be a whole number (0, 1, 2, etc.).
These assumptions are necessary to apply the distribution accurately. Violation of these assumptions may require the use of alternative probability distributions.
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