Another important aspect of data visualization is accuracy so as not to mislead the audience with how the data are presented. Ensuring your data set is comprehensive, accurate, and unbiased is the first step before beginning to analyze it. Understanding the difference between correlation and causation is critical when drawing conclusions. Correlation is a statistic that describes the relationship of two or more variables. Two sets of data may be strongly correlated but a change in one may not be the cause of a change in the other. Whereas with causation, one event is the result of the occurrence of the other event. (Statistics). A popular example of this in healthcare is that smoking is correlated with alcoholism but is not the cause of alcoholism. However, smoking does cause an increase in the risk of developing lung cancer. (iPerceptions, 2016). If you're not careful, it's easy for correlation to confound the results of a study