Psyb10 lecture 02 the self & self regulation. The outcome, the variable you want to be able to predict (b) independent variable iv". The predictor, the variable that you think will predict the dv, the independent must be experimentally manipulated in order to imply causation: correlational design, key features: 2 dvs. We consider both variables in a correlational analysis to be outcomes to reflect the lack of causal conclusions that can be drawn. Examples of correlational design: ice-cream sales are strongly correlated with increased number of drowning. This might not be true because it does depend on a number of factors which might be related to the increased number of drowning other than ice creams. If 2 things are correlated this means that they predict each other. E. g. assuming that someone is sick because they have stuffy nose, which might be right. However, stuffy nose didn"t cause the cold, but vice versa: statistical analysis for correlational design is correlation.