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).