One of the things which make statistics confusing is the confusing language. There is the triple negative of "Fail to Reject the Null Hypothesis". There are several instances where a single concept has multiple names. This tip is about what one might call an "asymmetry" in concept names. - Beta,
*β*, is the probability of a Beta ("False Negative") Error - So, one would think that "Alpha",
*α*, would be the Probability of an Alpha ("False Positive") Error. Right? - Wrong!
*p*, the*p*-value, is the Probability of an Alpha Error.
So, what is Alpha? First of all, the person performing the a statistical test selects the value of Alpha. Alpha is (called the "Significance Level"). It is 1 minus the Confidence Level. Alpha is the maximum value for p(the probability of an Alpha Error) which the tester is willing to tolerate and still call the test results "Statistically Significant". For more on Alpha and
p, you can view 2 videos on my YouTube channel http://bit.ly/2dD5H5f- Alpha, the Significance Level https://youtu.be/rl_J9UTXiMA
- p, p-value
__https://youtu.be/vyX4m89VkyI__
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## AuthorAndrew A. (Andy) Jawlik is the author of the book, Statistics from A to Z -- Confusing Concepts Clarified, published by Wiley. ## Archives
December 2019
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