Understanding dependent and independent events is important for computing probabilities in statistics, data science, and everyday decision-making. The definition of independent events relies on conditional probability: Two events $A$ and $B$ are independent if the occurrence of event $B$ does not affect the probability of event $A$, and this is expressed as $$P(A|B) = P(A),\qquad \qquad P(B|A)=P(B)$$ If this equation does not hold, the events are dependent. Independent means the occurrence of event $A$ gives no information about whether event $B$ happened. Independent Events Real-Life Examples of Independent Event Dependent Events…

In this post we will learn about the use and application of Binomial Distribution in R. The binomial probability distribution is used to model the number of "Successes" in a fixed number of independent trials, where every trial has only two possible outcomes (such as success or failure, true or false, right or wrong, and yes or no, etc.), whereas the probability of success remains constant/ fixed.