By the end of this chapter, you will be able to do the following:

8.1 Identify appropriate ways to address missing data, including assumptions considered, criteria for exclusion, and specific techniques used.

8.2 Choose the most suitable way to transform your data, if needed (e.g., nonlinear transformations to normalize scores).

8.3 Articulate how to define, identify, and handle the outliers in your data.

8.4 Plan how to identify and handle error outliers in your data.

8.5 Verify if your data contain interesting outliers and consider how you will deal with them.

8.6 Formulate your decision-making process for defining, identifying, and handling influential outliers in regression, structural equation modeling, and multilevel modeling.

8.7 Prepare your hypotheses while avoiding HARKing and consider how various forms of HARKing may affect your data.

8.8 Avoid unintentional cherry-picking and question trolling when implementing multivariate procedures.