This paper presents evaluations and comparisons of statistical methods practiced in factor screening as well as in analysis with missing data. In quality performance evaluation studies using factorial experiments, the method of a normal probability plot is routinely used for screening out factors with insignificant effect on the quality characteristic. We demonstrate that the method of a normal probability plot for factor screening can be misleading. A screened out factor may turn out to be significant in the presence of certain interactions. Three methods of analysis in the presence of missing data for explanatory or response variables are considered under the multiple linear regression model by ignoring the missing data rows, substituting the corresponding column means from the data rows with no missing data, and substituting the Yates/EM algorithm estimates for the missing data. Five illustrative examples are given. In all examples, the third method has turned out to be the winner.