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Among most researchers, and particularly those in the pharmaceutical industry, your alpha (Type I error) should be <=0.05 and your beta (Type II error) should be =>0.80. The alpha reflects the typical "p < 0.05" seen in journal reports and is the error of rejecting a null hypothesis when it is actually true. The beta (Type II error) is its reflection, the error of not rejecting a null hypothesis when the alternative hypothesis is true, and also referred to as power. Both are set so that sample size can be calculated with these parameters in mind. There are good internet sample size calculators using these parameters -- here's one: http://www.stat.ubc.ca/~rollin/stats/ssize/n2.html Hope this helps.