### Albert Steppi

Albert Steppi (@steppi) is a Senior Software Engineer at Quansight Labs. He earned a PhD in Statistics from Florida State University in 2018. Albert has been a maintainer of the SciPy library since 2021.

#### Sessions

Resampling and Monte Carlo statistical techniques are surprisingly intuitive, and they are often more flexible and accurate than their better-known analytical counterparts. In this tutorial, participants will develop their intuitive understanding of frequentist statistics and apply it using three functions in `scipy.stats`

- `monte_carlo_test`

, `permutation_test`

, and `bootstrap`

- to dramatically expand the statistical analyses they can perform with the SciPy Library.

"Intuitive Statistics" may sound like an oxymoron, but perhaps this is because traditional academic treatments of hypothesis tests and confidence intervals focus either on a set of procedures to memorize or, at a more advanced level, their formal underpinnings. However, statistical resampling and Monte Carlo techniques are surprisingly understandable, and these techniques are often more flexible and accurate than their better-known counterparts. This poster presents three functions in `scipy.stats`

- `monte_carlo_test`

, `permutation_test`

, and `bootstrap`

- which are relatively simple to understand, yet flexible enough to replace almost all other hypothesis test and confidence interval functions in the SciPy library.