Hypothesis Testing Explained
Hypothesis testing is super-confusing for every student, right up until the day that you "get it", at which point it becomes a simple matter of plug-and-chug. This chapter is one you MUST WATCH if you are doing hypothesis testing, because its only purpose is to get you to that magic "I get it" moment sooner rather than later. If you're confused by hypothesis testing, forget everything you heard in class and just watch these videos in order. You'll be glad you did!
The Logic of Hypothesis Testing (1 of 5)
This video is the first in a series explaining the basics of hypothesis testing. It will help you understand the basic concept behind every hypothesis test questions, which is always this: They give you some data that APPEARS to show some effect (a new medicine is better than the old one, the coin is weighted towards heads, things are different than in the past, etc.). But what if the effect is due to random luck (a.k.a. sampling error) as opposed to the effect being real? Well, you calculate the odds of it being due to luck, and if that's a low probability, you assume the effect must be real.
Null & Alternative Hypothesis (H0 & Ha)
(video 2 of 5)
This video is the second in a series explaining the basics of hypothesis testing. In this particular video, we get practice doing the first step of all hypothesis test problems: writing down the null and alternative hypotheses (H0 & Ha). I don't even do any calculations; this video is strictly about making hypothesis writing a plug-and-chug affair.
One-Tailed vs Two-Tailed Tests (3 of 5)
This video is the third in a series explaining the basics of hypothesis testing. In it I explain what 1-tailed and 2-tailed tests are, and how it affects your calculations of critical values and confidence levels.
Type 1 & Type 2 Errors (4 of 5)
This video is the fourth in a series explaining the basics of hypothesis testing. In it I explain what Type I and Type II errors are. I also give you the "routine for fun" memory trick for keeping the two types straight, as well as how to use this "trick", which is one of the hardest-to-use memory shortcuts I've ever seen. As always, I'll try to make it plug-and-chug for you!
Power Of A Test (5 of 5)
This video is the first in a series explaining the basics of hypothesis testing. The power of a test -- often known as "powering a test" -- is the basic idea that if you want better data, you need larger samples. But getting larger samples is either more work, more expensive, or both, especially if you already collected data which turned out to be inconclusive. So you need to figure out ahead of time how big a sample to collect, and that is the crux of powering your test: how big to make your sample so that you won't come up short.
"If p is low, the null must go"
This is a silly saying that I only discovered lately but which allegedly helps some students remember whether P is supposed to be big or small in a hypothesis test. To me it just sounds like a made up quote from Jackie Chiles on Seinfeld (a character based on O.J.'s lawyer, Johnny Cochran), but hey, that doesn't make it wrong.