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In another class, I have recently been studying the effect of extreme groups or differing subgroups within a sample- particularly how differing results within subgroups may not be accurately represented by the resulting correlation coefficient.

I did not consider that this can be related to the issues with gathering data that we have been discussing in Data Cultures. In response to previous articles, we have concluded that large conglomerations of individual data, while useful, may omit aspects of the individual that are key in the analysis of the data. Simpson’s paradox provides a clear and concise way of stating and demonstrating this effect.

For example, in the civilian casualties reading, nuisance crimes and more serious crimes were treated/ analyzed on the same level. As a result, people who committed petty crimes and areas in which these occurred received the same amount of attention from law enforcement as more violent/ serious crimes. There are two factors that this form of analysis omits: nuisance crimes are of a smaller scale and thus occur more often, so areas in which these occur were likely flagged even more than areas with dangerous crime. In addition, this means that people who commit smaller scale crimes were being institutionalized at higher rates, whereas the people who are likely committing dangerous crimes out of malicious intent (not due to SEO or other related factors) are not prosecuted at the same rate.

The article discussing Simpson’s paradox raised concerns of a similar magnitude, such as medical conclusions (ie inaccurate conclusions pertaining to effective dosage). Such high-importance examples, particularly ones that have been published and effect people’s behavior related to these topics, demonstrate the importance of scrutiny and close observation when analyzing data.