Statistical Fundamentals (3 of 8): P Value, Significance, and T-test

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.

P Value, Significance, and T-test (workshop 3 of 8): This session will introduce participants to the concept of P values and their role in hypothesis testing, highlighting that P values reflect the probability of observing the data under the null hypothesis, and not necessarily the biological significance of the findings. The session will cover the computation of P values and delve into the nuances of one-sample t-tests.

By the end of the session, participants should be able to comprehend the meaning of P values, understand how hypothesis tests calculate P values, recognize when small P values indicate unlikely events under the null hypothesis, and explore the assumptions behind one-sample t-tests.

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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.