Common sources of errors were discussed, and typical examples were presented. Here it was also outlined that if not well documented, this step can "Optimise" the data to make "good" data fit prior expectations or hypotheses.

The next step discussed was data screening. This step includes analysing the distributions of the variables and missing data. The central part of the presentation addressed the handling of missing data through single and multiple imputation methods and the different concepts of missing data mechanisms such as MCAR (missing completely at random), MAR (missing at random) and MNAR (missing not at random). The presentation concluded with standard ways to report initial data analysis in research.