Statistical Fundamentals: A Visual Approach Workshop Series—Visualizing Samples with Boxplots and Kick the Bar Chart Habit

This session will address the advantages of box plots over bar charts for displaying the spread and variability in data. Participants will learn how box plots can be used to compare multiple samples, the impact of sample size on data representation, and the efficient identification of outliers.

By the end of the session, participants should be able to create and interpret box plots, appreciate their usefulness in comparing multiple samples, understand the implications of sample size, and identify outliers and median confidence intervals through notches in box plots.

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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