Click a card to master the distributions of your business.
Tap on any card to see the Why, When, and How.
A binary outcome: Success (1) or Failure (0).
To model individual yes/no events before they happen.
When you focus on a single person walking past your door.
Define $p$ (probability they enter). If $p=0.2$, there's a 20% chance they buy.
Sum of multiple independent Bernoulli trials.
To predict outcomes for a fixed-size group of people.
When 50 people walk by and you need to know how many might come in.
Use number of trials ($n$) and probability ($p$) to find the likely "count."
Frequency of events over a continuous interval (time).
To manage staffing levels for unpredictable arrivals.
When you want to know the chance of 100 people arriving between 8 AM - 9 AM.
Based on your historical average ($\lambda$). If you average 40/hr, what's the chance of 60?
The Bell Curve. Symmetric data clustered around a mean.
For Quality Control and predicting natural variations.
When measuring the exact weight of coffee beans in every bag.
Use the Mean ($\mu$) and Standard Deviation ($\sigma$) to see if a bag is "too light."
The time between independent events.
To optimize workflow and customer experience.
When the barista asks: "How long until I have to make the next latte?"
Calculates the probability that the "gap" between customers is $> 5$ minutes.
Every outcome in a range has the exact same probability.
When there is no clear peak or "popular" time in a window.
A supplier says they will deliver milk "between 1 PM and 4 PM."
The probability is spread flat across the entire time range.