The most commonly used values of Alpha levels are 0.10, 0.05, and 0.01. 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 most commonly used values of Alpha levels are 0.10, 0.05, and 0.01. 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 (Taylor, 2016).

There are different instances where it is more acceptable to have a Type I error. 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 (Taylor, 2016).

There are some instances in which we would need a very small p-value to reject a null hypothesis. If our null hypothesis concerns something that is widely accepted as true, then there must be a high de gree of evidence in favor of rejecting the null hypothesis. This is provided by a p-value that is much smaller than the commonly used values for alpha (Taylor, 2016).

 

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