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The article that addresses Simpson’s Paradox begins by addressing the problem with people’s observations of data. It most clearly describes how data can derive different conclusions if the data is split into sub-groups versus all of the data being kept together. The graph that analyzes Alcohol Intake and IQ levels is extremely interesting, it is extremely evident that within the group of people, the people with higher IQ’s consume more alcohol but within each individual the less alcohol they consume the higher their IQ. This proves Simpson’s Paradox that the overall conclusion goes against what is really true. This article gives many good examples of this paradox being apparent in data.  By going on to address how it should be dealt with there are many good suggestions of how scientists should be aware of their data and how they should assess outcomes like this. I think the idea of looking at clustering within data to see if trends might be going in conflicting ways is one of the best ways to address this issue. They conclude that data has to be analyzed very carefully in order to make the correct conclusions. I thought this article did a good job of presenting examples of Simpson’s Paradox and explaining the errors is conclusions that can be made because of this. It is an interesting topic because it is probably extremely present in many data sets. Last week Jackson Hayes addressed how in data can be manipulated “I was also intrigued by the way data can easily vary based on different variables.” I think this relates to simpson’s paradox because it shows how data in many ways can represent the wrong things. All in all I think both articles bring important aspects of data analysis to light.