A first look at hypothesis testing.
An introduction to Z tests for a population mean mu. These tests are appropriate when sampling from a normally distributed population where sigma is known. I discuss the hypotheses and the underlying logic, and work through an example. I do not discuss the rejection region or p-value in an in-depth way in this video – … Read more
I work through a few quick examples of determining what area corresponds to the p-value in one-sample Z tests on the population mean mu. The same logic holds for other types of test. I assume that you know how to find areas under the standard normal curve.
An introduction to the concept of the p-value in a Z test. This video discusses the p-value in the context of a Z test for one mean, but the same logic holds for other Z tests.
An example of a Z test on the population mean mu. This test is appropriate when sampling from a normally distributed population, when the population standard deviation sigma is known.
An introduction to the concept of the p-value, in the context of one-sample Z tests for the population mean. Much of the underlying logic holds for other tests as well. I discuss what a p-value is, then using simulation I illustrate its distribution when the null hypothesis is true and when the null hypothesis is … Read more
A discussion of Type I errors, Type II errors, their probabilities of occurring (alpha and beta), and the power of a hypothesis test.
A discussion of when to use a one-sided alternative hypothesis and when to use a two-sided alternative hypothesis in hypothesis testing. I assume that the viewer has already had a brief introduction to the notion of one-sided and two-sided tests.
A brief discussion of the meaning of statistical significance, and how it is strongly related to the sample size.
I discuss the relationship between a (two-sided) confidence interval and a two-sided hypothesis test. I discuss the relationship in terms of inference for one mean, but the same concept holds in many other settings as well.