Identifying the distribution of your data is a key step in any analysis. It might be binomial or Poison, for example.

The binomial distribution is a discrete distribution that models the probability of events with only two possible outcomes, success or failure. There are two assumptions.

• Each event is independent or does not affect the probability of the others
• The probability of success is the same for each event

Coin Toss Example

Let’s toss a coin and get heads or tails as an outcome. Let’s define “heads” as success. , it’s important to know that they must be mutually exclusive. Two outcomes are mutually exclusive if they cannot occur at the same time. You can’t get both heads and tails on the same coin toss. Let’s toss it 20 times in a row. This is a binomial experiment.

Data professionals use the binomial distribution to model data in different fields such as medicine, banking, investing, and machine learning. data professionals use binomial distribution to model the probability that a new medication generates side effects, a credit card transaction is fraudulent, or a stock price rises or falls in value. In machine learning, the binomial distribution is often used to classify data.

Random Experiment

A random experiment is a process whose outcome cannot be predicted with certainty. A binomial experiment is a type of random experiment. All random experiments have three things in common.

• The experiment can have more than one possible outcome.
• You can represent each possible outcome in advance
• The outcome of the experiment depends on chance.

Binomial Experiment

a binomial experiment has the following attributes.

• The experiment consists of a number of repeated trials
• Each trial has only two possible outcomes.
• The probability of success is the same for each trial
• Each trial is independent.

Predicting Sales Returns Given Sales Volume

Suppose you want to predict the number of times in a day that a customer will return something. You have discovered from historical data that 10% of customers return something. You label a return as a success. Suppose 100 customers visit a store in a day. It’s important to understand the features of a binomial experiment because the binomial distribution could only model data for this type of event.

Imagine that three customers visit the store. You label a return as a success. You can use the formula to determine the probability of getting 0, 1, 2, and 3 returns among the three customers.