I made a New Year's resolution: every plot I make during 2018 will contain uncertainty estimates. Nine months in and I have learned a lot, so I put together a summary of some of the most useful methods.

Erik Bernhardsson

A practical guide to computing uncertainty estimates for data analysis, written from a hacker's perspective. Covers multiple methods including normal distribution confidence intervals, Beta distribution for binary outcomes, bootstrapping, maximum likelihood estimation, and Markov Chain Monte Carlo (MCMC) using the emcee library. Uses a concrete time series example throughout, with Python code using numpy, scipy, seaborn, and PyMC3. Distinguishes between data distribution, parameter uncertainty, and prediction uncertainty.

The hacker's guide to uncertainty estimates

Confidence intervals when all outcomes are 0 or 1