Distributions in the Wild
Recognise the common distributions in real data and know what each implies
A taste of a lesson
Our support calls average 12 per hour. Can I use that to plan staffing for busy hours?
An average alone will under staff you. If calls arrive independently at a steady rate, counts per hour roughly follow a Poisson distribution, and with a mean of 12 you would see 18 or more calls in some hours fairly often, roughly 1 hour in 16. Real call centres also have busier times of day, which makes variation even larger. So look at the distribution for each hour of the day separately. Quick check: what is the highest hourly count you have seen in the last month?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Recognise common distributions from histograms and context
- Link each distribution to the process that generates it
- Explain the central limit theorem in plain words
- Check a distribution with histograms and quantile plots
- Choose sensible summaries for skewed and heavy tailed data
Lesson plan
- 1 Shapes and stories Describe a distribution's shape and connect it to how data arose. Start
- 2 The normal distribution Use the normal distribution where it fits and know the 68 95 99.7 rule. Start
- 3 Counting successes and events Recognise binomial and Poisson counts. Start
- 4 Waiting times and skew Understand exponential and log normal shapes. Start
- 5 Heavy tails Spot data where rare extremes dominate. Start
- 6 The central limit theorem Explain why averages behave well even when data does not. Start
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About this tutor
A beginner tutor that introduces probability distributions through the data you meet at work. You will learn to recognise normal, binomial, Poisson, uniform, exponential and heavy tailed shapes, connect each to the kind of process that produces it, and understand why the shape changes which summaries and methods make sense. Lessons cover the central limit theorem in plain words, why many real quantities are skewed, and how to check a distribution with a histogram and a quantile plot. Each lesson uses a realistic example such as call volumes, waiting times or order sizes.
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About the teacher
Statistics in plain language, from averages to Bayesian reasoning
9 tutors 350 lessons taught Sample
I teach statistics to people who were put off by it the first time. My approach is to start from a question someone actually has, simulate or count our way to an answer, and only then name the formula. I have worked as an analyst on survey and health research projects, so I have a soft spot for messy samples,...
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