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Money, profit, and success define our world today whether we like it or not. All research and studies are conducted with a motive in mind. In Kievit’s  Simpson’s Paradox article, treatment dosage and recovery percentage is analyzed among male and female samples. The results reach a fairly contradictory conclusion, that both men and women have a negative relationship, meaning that as you increase the treatment dosage, the recovery percentage is lowered. At first glance, one might believe this to sound utterly ridiculous. But if you think about this relationship more closely, you realize that there is omitted variable bias taking place. Omitted variable bias occurs when “a variable that is correlated with both the dependent and one or more included independent variables is omitted from a regression equation.” In this example, an omitted variable is preexistent health. For example, the majority of people who need a low dosage are probably pretty healthy, so their recovery percentage is high as opposed to the people who need a very high dosage. These individuals might already have some preexisting health conditions that are very serious affecting their recovery percentage. This isn’t captured in the data, which can distort the results. Again, here, we see that data isn’t always accurate and can be easily manipulated to tell a story. For example, coffee manufacturers want to release information from studies indicating that coffee consumption is good for your health to increase consumption and profit.