If the statistics for normality turn out to be significant in my analysis, does that mean I cannot use parametric tests any more?
Not necessarily. Statistics of normality do reveal whether a data distribution is normal or not, and help determine whether parametric or non-parametric tests should be used, or data transformation is needed. However, since NHANES is a large, representative sample of the U.S. population, most continuous variables from this sample are expected to be normally distributed. If you just conduct tests for normality, results on most variables would be significant, i.e. even the slightest deviation from normality could result in rejecting the null hypothesis due to the extremely large sample sizes. Therefore, you should not solely rely on these tests for normality to base your decision on. A Q-Q plot, or a quantile-quantile plot, may offer additional information. Q-Q plot is a graphical data analysis technique for assessing whether the distribution for data follows a particular distribution. In a Q-Q plot, the distribution of the variable in question is plotted against a normal distribution. T
