Statistical Fundamentals (1 of 8): Population Distributions, Sampling Distributions, and Central Limit Theorem

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.

Population Distributions, Sampling Distributions, and Central Limit Theorem (workshop 1 of 8): This session will introduce participants to the foundational concepts of statistical inference, including population distributions, sampling distribution, and the process of random sampling.

By the end of this 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.

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For a complete list of upcoming CSC Workshops, please visit our workshops page.

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

Questions? Contact the Centre for Scholarly Communication at csc.ok@ubc.ca.