4 episodes listed below
A real statistics classroom, arithmetic done out loud.
Classroom recordings from Psychology 3256 work through t-tests and factorial ANOVA by hand, using small data sets built so the arithmetic stays clean. The instructor explains why each test works as well as how, checks his sums aloud, and breaks up the math with dry, self-mocking asides.
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Dr. Dave Brodbeck's Statistics Videos is a set of classroom recordings from Psychology 3256, a statistics course at Algoma University. What a listener gets is the class itself: a lecture aimed at the students in the room, sometimes ending with 'Questions?'. The episode titles give the term and the topic, and the excerpts cover two core methods, t-tests and factorial analysis of variance. The t-test session is framed as catching everyone up. The instructor says the class already knows the material, but going back to it lets him show how a test works and why, an approach he says the rest of the course will share. He begins with the normal distribution. Turn a value into a z score, look it up in the table, and you are golden. Then comes the catch. Z is easy, he admits, and that is a shame, because here it will not do the job. The session ends on independence of observations: nothing in the math changes if you happen to be Mike, and breaking that assumption means leaving t-tests behind altogether. He calls the factorial ANOVA lecture 'the fun stuff'. Its running example is a memory study on retention interval, with recall scored as percent correct after five minutes, one hour and 24 hours, and he mentions that students in the lab class could run it without much trouble. The data are made up, and he believes he invented the numbers back in 1996. The one thing rigged about them is that they avoid fractions. From there he works through it aloud, one step at a time. He removes the grand mean, then the effects for the a factor, and checks along the way that each set sums to zero. That is not magic, he says, just a property of numbers, and it means you can check your work at every stage. The humour is dry and aimed at himself. He wonders whether he can do arithmetic in his head and answers that yes, he can, and he calls switching the order of the factors 'a little spice'. The asides can wander a long way. One story about his daughter's grade two teacher and an hour of homework a night ends in a Donald Trump comparison. The ANOVA session ends with a practical warning: keep adding factors and the number of participants needed climbs past a thousand, which he calls a nightmare. The t-test lecture exists in both a Winter 2012 and a Winter 2013 recording, and the excerpts match almost line for line. Since that lecture presents itself as a refresher that sets up the method for the rest of the course, it is a sensible place to start.
Made for: Suits a listener taking a university statistics or research methods course who wants t-tests and factorial ANOVA worked out by hand, with each step explained. Also a good fit for someone who learns better from a real classroom than from a polished explainer.
What sets it apart: These are recordings of an actual class, so the arithmetic happens in real time, on numbers chosen to avoid fractions and with checks such as zero-sum effects built into every step. The instructor's asides, from an example data set he dates to 1996 to a story about grade two homework, give it a personality that a textbook walkthrough lacks.
Video presentations (with audio) of Dave Brodbeck's class 'Psychology 3256 - Design and Analysis' from Algoma University. A course on ANOVA
Short reviews from the PlayNext desk, based on the episodes we processed.
A worked memory experiment makes factorial ANOVA feel concrete, checkable, and easy to see at scale.
Factorial ANOVA comes into focus through a memory experiment organized around retention intervals of five minutes, one hour, and 24 hours. The review tracks how the episode begins with percent-correct recall, then broadens the problem so retention interval is seen as one part of…
A conversational t-test lesson that makes statistical assumptions concrete, memorable, and wryly human.
This statistics lecture uses the t-test to step beyond the familiar ease of z scores, grounding the method in normal distributions, the population mean, standard deviation, and probability tables.
A grounded t-test lecture that moves from z tables to violated assumptions, with hockey cards at the edge.
The t-test lecture opens from familiar ground: a normally distributed variable, a score converted into a z score, and a z table used to find probability.
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