Uploaded February 2026 | Updated September 2026, 2 hours ago
This is a full lecture-style video introducing hypothesis testing, pitched at the level of an applied introductory statistics course at university. The focus is on big-picture concepts rather than the mechanics of any specific test.
Here I work through the start of my lecture outline document, a condensed version of the hypothesis testing chapter from my textbook. Students in my STAT I course at the University of Guelph have these materials.
If you're looking for a quick procedural walkthrough, this isn't the right video for you; these lectures are about statistical thinking. I have many shorter videos dedicated to specific topics that may be more appropriate.
References for the examples:
Manconi et al. (2010). Measuring the error in sleep estimation in normal subjects and in patients
with insomnia. Journal of Sleep Research, 19:478–486.
Fink et al. (2007). Male facial appearance signals physical strength to women. American Journal
of Human Biology, 19:82–87
This is a full lecture-style video introducing hypothesis testing, pitched at the level of an applied introductory statistics course at university. The focus is on big-picture concepts rather than the mechanics of any specific test.
Here I work through the start of my lecture outline document, a condensed version of the hypothesis testing chapter from my textbook. Students in my STAT I course at the University of Guelph have these materials.
If you're looking for a quick procedural walkthrough, this isn't the right video for you; these lectures are about statistical thinking. I have many shorter videos dedicated to specific topics that may be more appropriate.
References for the examples:
Manconi et al. (2010). Measuring the error in sleep estimation in normal subjects and in patients
with insomnia. Journal of Sleep Research, 19:478–486.
Fink et al. (2007). Male facial appearance signals physical strength to women. American Journal
of Human Biology, 19:82–87



![Confidence Intervals for One Mean: Sigma Not Known (t Method)
Introduction to confidence intervals for mu based on the t distribution. These methods are appropriate when we are sampling from a normally distributed population and the population standard deviation (sigma) is not known.
The cereal data used in this video is real data from a sample of 7 cereal boxes I purchased one day. The boxes had a stated weight of 750 grams. (Ive changed the story slightly in the video, but it is real data.)
For those that use R, below is the R code to find the values given in the video.
With 6 degrees of freedom, the t value with an area of 0.025 to the right (and thus an area of 0.975 to the left), can be found with:
qt(.975,6)
[1] 2.446912 Confidence Intervals for One Mean: Sigma Not Known (t Method)](https://i.ytimg.com/vi/bFefxSE5bmo/mqdefault.jpg)






