Nobody is perfect
Or, in other words, we all make errors. Let’s see the types we can make:
Suppose you are testing the effectiveness of drug A compared to drug B. There are two real possibilities: either there is a difference between them or not.
After you perform the test, there are two possibilities: either you FIND a difference, or not.
You could put that in a 2x2 table:
Real difference No real difference
Computer says YES Well done! Type I error
Computer says NO Type II error Well done!
There are two ways you can go wrong. If you affirm there is a difference between A and B, and there is not, you have a type I or alpha error. If you are not able to find a real difference between A and B, you have a type II or beta error.
You can put it in other words:
If you reject a null hypothesis but you should have accepted it, you fall into a type I or alpha error. If you accept a null hypothesis but you should have rejected it, that’s a type II or beta error.
There is a mnemotechnic for it; Beta stands for Blind. (Beta error means you are blind to see a real difference between the two drugs).
Thanks for reading!
Evidence Based Healthcare and Biostatistics for those who, as many doctors and health workers, hate numbers (thus the name of the blog). It aims to be a way to share ideas, resources, tools and training links, and a place to discuss specific health issues from an epidemiological perspective. Our desktop is a portrait of Karl Pearson (1857 - 1936), a prominent figure in Statistics and author of the book "The Grammar of Science" Please Share your comments!
Showing posts with label statistical error. Show all posts
Showing posts with label statistical error. Show all posts
Monday, January 16, 2012
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