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Statistical Fundamentals: A Visual Approach Workshop Series—Population, Sampling, Sampling Distribution and Central Limit Theorem

February 1 at 2:00 pm - 3:00 pm


This session will introduce participants to the foundational concepts of statistical inference, including population distributions and the process of random sampling. Attendees will learn how sampling distributions evolve towards normality as sample sizes increase and will visually explore the Central Limit Theorem.

By the end of the session, participants should be able to visualize and understand population distributions, illustrate random sampling processes, recognize the normalizing effect of larger samples on sampling distributions, and demonstrate the Central Limit Theorem visually.

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

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.

Register Now


February 1
2:00 pm - 3:00 pm


Library Building (LIB)
3287 University Way
Kelowna, BC V1V 1V7 Canada
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Additional Info

Room Number
Registration/RSVP Required
Yes (see event description)
Event Type
Research and Innovation, Student Learning
Students, Postdoctoral Fellows and Research Associates