Updated: I abhor Lilliefor and other tests of normality (created 2005-04-14).
Someone asked me about a log transformation for their data. It seemed like a good idea, based on my general comments on the log transformation, but the test of significance for normality (Lilliefor's test) was still rejected even after the log transformation.
In general, I dislike Lilliefor's test (and other tests of normality like the Shapiro-Wilks test). They have way too much power power for large sample sizes and will often end up detecting trivial departures from normality. Instead of a formal test, use a histogram, boxplot, normal probability plot, or whatever to get a qualitative indication of how close your data is to a normal distribution.
This page was written by Steve Simon while working at Children's Mercy Hospital. Although I do not hold the copyright for this material, I am reproducing it here as a service, as it is no longer available on the Children's Mercy Hospital website. Need more information? I have a page with general help resources. You can also browse for pages similar to this one at Category: Modeling issues.