Stats
I abhor Lilliefor and other tests of normality (April 14, 2005)
Category: Modeling issues
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.
Further reading
07/08/2008.
