Statistical Fundamentals: A Visual Approach Workshop Series—Non-parametric Tests and Visualizations

This session will introduce participants to non-parametric tests, which are useful when data distributions do not meet the assumptions of parametric tests. Attendees will learn to compare the adaptability of these tests with different data distributions and to visualize their operation.

By the end of the session, participants should be able to choose and apply the appropriate non-parametric tests for their data, visualize the operation of these tests, and understand the challenges of multiple testing with non-parametric methods.

This workshop qualifies for the Scholarly Research, Writing, and Publishing Credential offered through the College of Graduate Studies.

Workshop Leader: Nijiati Abulizi, Centre for Scholarly Communication Data Consultant

Nijiati specializes in data wrangling and visualization and statistical analysis. He provides support with R (both base R and tidyverse), RStudio, R Markdown (including bookdown), Python and Jupyter Notebook, Git and GitHub, and QIIME.

Statistical Fundamentals: A Visual Approach Workshop Series

This series will use R and Python to help develop an intuition for fundamental statistical concepts using data visualization. These workshops are equally suitable to those hoping to enhance their ability to interpret common statistical tests and concepts as it is for those applying statistical modelling to their work. No background in statistics is required, but some familiarity with R or Python will be advantageous.

You may wish to review the asynchronous content of either R Fundamentals for Data Analysis or Python Basics for Data Analysis, or keep an eye out for the next time these workshops are offered.

You can either register for the whole series, or register for individual workshops that are most applicable to you.

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