Before you analyze your data, it is very important that you **check
the distribution and normality** of the data and identify outliers
for continuous variables with a univariate analysis. If the
distribution is **highly skewed**,
you can do a data transformation to make the distribution of the
data closer to normal (the underlying assumption in most statistical
analyses is that the distribution of the data is normal). The common
types of transformation are LOGIT, LOG, LOG10, SQRT, INVERSE, or ARCSIN.
Transforming data should be covered in any basic biostatistics text
and will not be covered in detail in this tutorial.

After checking the distribution and normality of the data, plot the survey
weight against the variable to determine which of the extreme values identified
in the univariate analysis are outliers. You must also determine if the outliers
represent **valid values** and, if so, also carry **extremely large survey
weights**. Outliers with extremely large
weights could have an influential impact on your estimates. Then you have to
decide whether to keep these influential outliers in your analysis or
not. It is up to the **analysts to make that decision**. Please consult
the
Analytical Guidelines for more information on this topic.