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Category: Hypothesis testing. Hypothesis testing is a set of formal methods to select between two competing research hypotheses. These pages discuss some of the philosophical underpinnings for hypothesis testing as well as some pragmatic concerns. Articles are arranged by date with the most recent entries at the top. You can find the theme and closely related categories, definitions, and other resources at the bottom of this page.
Stats: An alternative to the p-value (April 3, 2008). A discussion on edstat-l concerned a statistic called p-rep. I had not heard of this statistic before, but at least one journal is calling for its use in all papers published by that journal.
Stats: What is a critical value? (February 22, 2008). Someone wrote in asking about the difference between a p-value and a critical value.
Stats: Type III error (January 3, 2008). Dear Professor Mean, What is the definition of a Type III error?
Stats: Further exploration of Type I and Type II errors (April 5, 2007). I got some feedback that my definitions of Type I errors and Type II errors would be clearer if I specified what the actual hypothesis are. I wanted to avoid symbols like mu or pi, so here is what I wrote.
Stats: Type II error (September 3, 1999). Dear Professor Mean: A journal reviewer criticized the small sample size in my research study and suggested that I mention a Type II error as a possible explanation for my results. I've never heard this term before. What is a Type II error?
Stats: T-test (April 18, 1999). Dear Professor Mean: How do you analyze a t-test? I have a t-test value, and I know that I have to compare it to a t-distribution. I'm not sure how to do that.
Theme and closely related categories:
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This webpage was written by Steve Simon on 2007-06-16, edited by Steve Simon, and was last modified on 2008-07-08. Send feedback to ssimon at cmh dot edu or click on the email link at the top of the page.