Lecture 10: Confidence Intervals (part 2)

October 6th, 2022 (5th week of classes)
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Key statistical details to understand confidence intervals and other critical statistical methods

The goal of this lecture is to provide the statistical details underlying confidence intervals. This details will be also critical to build intuition and knowledge about most statistical methods used in biology and across all fields.

Recorded lectures

The slides were taken from previous and future lectures so there are no pdf files with the slides. The lecture also involve a combination of R programming and concepts. Watch the lecture and take notes. That’s really the best way to work on this lecture in particular. And you can always stop the video and take screenshots of particular frames and annotate.

part 1

General theory: from a computational approach to develop sampling distributions based on a limited number of sample means to a general statistical theory that can be used for “infinite” (a really large number) of sample means.

part 2

Statistical populations vary in terms of their shapes (e.g., symmetric, asymetric, uniform, etc) and their mean and standard deviation values. How can then one distribution fit all the infinite possible types of distributions? Part 2 provides further mathematical details to make the mathematical theory seen in part 1 to work with any statistical population (under a very important assumption).

Introduction to the t Distribution (non-technical), by JBstatistics. This video provide some details on the t distribution that is widely used and we will revisit it many times in BIOL322.