Data can get incredibly complex. Especially when different perspectives present countering data points, it gets difficult to identify which information is most accurate. Simpson’s Paradox is an example of how data, recorded from two ends, spits out completely different results. Studying the acceptance rates for graduate school, data shows that less women get accepted than man. However, from the perspective of the university, the acceptance rate for women is higher than that for men. Simpson’s paradox is a prime example of why data should never be considered from just one perspective, nor should it be recorded to a limited extent. From the study on mortality rates in North Dublin workhouses we see similarities in data complexity. During the height of the famine a high proportion of “immigrants” were dying in workhouses compared to “Dubliners”. Data, here too, could be vary depending on perspective. Immigrants higher mortality could be defined by the poor treatment and malnutrition of the outsiders. But what if we looked at their origin? Through that we can look at how foreign genetic pools may disadvantage immigrants’ immunity against the given virus circulating Dublin during the famine. Here, different perspectives give different results; and these are trends we see in many data models. Last week Elizabeth Cullen discusses how the development of data models is often overlooked. Mentioning the influence data recorders can have on data outcomes she writes: “they wanted to ensure that the data was from a group of extremely similar people and a cohabiting relationship is what they decided would be a factor of a similar group of people that would provide good supporting data. Considering this and Simpson’s Paradox, as individuals absorbing data, we must remember the complexities and biases of data models.<\/p>\n","protected":false},"excerpt":{"rendered":"

Data can get incredibly complex. Especially when different perspectives present countering data points, it gets difficult to identify which information is most accurate. Simpson’s Paradox is an example of how data, recorded from two ends, spits out completely different results. Studying the acceptance rates for graduate school, data shows that less women get accepted than … <\/p>\n

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