# The number represented by alpha is a probability. The values of 0.10, 0.05, and 0.01 are the ones most commonly used for alpha. The level of significance of a hypothesis test is exactly equal to the probability of a Type I error. A Type I error consists of incorrectly rejecting the null hypothesis when the null hypothesis is actually true.

The number represented by alpha is a probability. The values of 0.10, 0.05, and 0.01 are the ones most commonly used for alpha. The level of significance of a hypothesis test is exactly equal to the probability of a Type I error. A Type I error consists of incorrectly rejecting the null hypothesis when the null hypothesis is actually true. The smaller the value of alpha, the less likely it is that we reject a true null hypothesis. A larger value of alpha, even one greater than 0.10 may be appropriate when a smaller value of alpha results in a less desirable outcome. There is not a universal value of alpha that should be used for all statistical tests. In medical screening for a disease, consider the possibilities of a test that falsely tests positive for a disease with one that falsely tests negative for a disease. A false positive will result in anxiety for our patient, but will lead to other tests that will determine that the verdict of our test was indeed incorrect. A false negative will give our patient the incorrect assumption that he does not have a disease when he in fact does. The result is that the disease will not be treated. Given the choice, we would rather have conditions that result in a false positive than a false negative. In this situation, we would gladly accept a greater value for alpha if it resulted in a tradeoff of a lower likelihood of a false negative (Taylor, 2015).