When we set a confidence level, what we are doing is setting the probability of Type I error. The null hypothesis can always be interpreted as "nothing special is happening" and Type I errors mean we think something special is happening when it isn't. In many cases, false positive are more disruptive than false negatives and we are more interested in limiting Type I errors than we are in limiting Type II errors. There will be more discussion of this in class on Oct. 15 and the note will also be posted here.
No comments:
Post a Comment