Hello i need a Good and Positive Comment related with this argument .A paragraph with no more 90 words.
Re:Topic 3 DQ 2
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).
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The article discusses the importance of alpha, the level of significance, in hypothesis testing. It states that alpha, represented by the values of 0.10, 0.05, and 0.01, is the probability of a Type I error, which refers to incorrectly rejecting the null hypothesis when it is actually true. The article highlights that a smaller value of alpha means a lower likelihood of rejecting a true null hypothesis. However, in medical screening for a disease, a false negative could result in the disease not being treated. Therefore, a greater value for alpha may be accepted to minimize the likelihood of a false negative. Ultimately, the choice between false positives and false negatives depends on the specific situation.