Jeremy Balka's statistics channel, containing some introductory statistics videos.
(I am an associate professor in the Department of Mathematics and Statistics at the University of Guelph. I have a PhD in statistics, and have taught introductory statistics courses on many occasions.)
I try to be concise, and use real data in the vast majority of cases. Some of these videos move at a quick pace, especially my earlier ones, and they are not designed to be lecture replacements. They may serve to clarify some concepts after lecture. If you want to get ahead, watch the relevant videos before lecture to get an introduction to the topic.
The videos are pitched at the level of an applied introductory statistics course to non-statistics majors.
I'll be updating this channel with new videos until I've filled out a full introductory statistics course (and I may possibly expand after that).
A complete list of videos, organized by topic, can be found at www.jbstatistics.com.
I don't understand the main purpose of part 13:44. Can you summarize and explain briefly the big concept here? Anw, thankyou for another great video <33
It's a different visual illustration of what independence is, and how if P(B | A) > P(B) , say, then P(B|A^c) < P(B). I don't think any summary I give here will be of use compared to what's in the video.
No. We are carrying out a probability calculation on the mean of a sample from a normally distributed population, where sigma and mu happen to be known. In this situation, X bar ~ N(mu, sigma^2/n) and (X bar - mu)/(sigma/sqrt(n))~N(0,1). Probabilities are found from the normal distribution.
Hi! I loved this video and it was very concise! I was just wondering for the last example at around 9:05 , why didnt we divide the significance level by 2? Isn't alpha basically the same thing as the confidence level, so if we do a two tailed test, wouldn't alpha be split amongst the two tails so it would be 0.025? Im just a bit confused, thanks!
If you were doing this for a sample size calculation, would the final sample size be 16 or 32? In other words, if you wanted to find the sample size needed for achieving 7.9% power in the example, would you enroll 16 or 32 participants?
While I personally lean towards two-tailed alternatives in the vast majority of situations, I think the use of a one-tailed procedure is reasonable in this situation. Before collecting the data, there was a strong belief (based on previous studies and information) that puerarin would have a tendency to reduce alcohol consumption. So it wasn't just a "ooooh, I think my new drug is better" sort of argument. (Or even worse, using the data to inform the choice of alternative.) Abusing the use of a one-tailed test can be a form of p-hacking, sure, but I don't think that happened in this study.
