It is also necessary to reiterate that the sample sizes generated by powerlog should be considered to be a lower bound. Although the sample sizes provided are valid for hypotheses testing with a specified power, methodologist are not in complete agreement as to how big sample sizes need to be to obtain stable estimates. Long (1997) suggests that sample sizes of less than 100 should be avoided and that 500 observations should be adequate for almost any situation. However, this leaves a relatively large gap between 100 and 500. If powerlog gives a sample size of less than 100, you might want to increase it to at least 100, just to be safe. Even if powerlog suggests an N of say 110, you might want to use an larger sample if you believe that you data might be problematic in any way.