4 Important Probability Distributions – Normal, Binomial, Poisson, Bernoulli
Summary
This content explains four key probability distributions—Normal, Binomial, Poisson, and Bernoulli—and shows how to visualize them using Python. Understanding these distributions can help Salesforce teams improve data-driven decision-making, predictive modeling, and analytics within the platform. Admins and developers can apply these concepts to analyze customer behavior patterns, forecast outcomes, or enhance Einstein analytics models. It offers practical insight into probability concepts that underlie many Salesforce AI and reporting tools.
Takeaways
- Recognize key characteristics of Normal, Binomial, Poisson, and Bernoulli distributions.
- Understand when to use discrete vs continuous probability distributions.
- Use Python libraries to visualize probability distributions effectively.
- Apply basic probability concepts to enhance Salesforce analytics and forecasting.
- Leverage probability distributions to better model and predict business outcomes.
A probability distribution specifies the likelihood of all possible outcomes of a random variable. The distribution may be classified as either continuous or discrete based on the values assumed by a random variable. Various forms of probability distributions exist. In this post, I will examine Normal, Binomial, Poisson and Bernoulli distribution and demonstrate how to utilize Python to plot it.