Type I errors, also known as false positives, occur when you see things that are not there. Type II errors, or false negatives, occur when you don’t see things that are there (see Figure below).
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“The primary product of a research inquiry is one or more measures of effect size, not p values.”
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“Statistical significance is the least interesting thing about the results. You should describe the results in terms of measures of magnitude – not just, does a treatment affect people, but how much does it affect them.”
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“Investigators must learn to argue for the significance of their results without reference to inferential statistics.”
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This is a fantastic example of Type I and II errors! I’m a physician who teaches statistics to my medical students and residents. I’d like to use this image in a presentation: do you allow use for non-commercial purposes (with attribution of course)?
Feel free!
Thanks 😀