The Simpson’s Paradox is interesting when related to large corporations or news sources, because many times the paradox is manipulated in their favor. This paradox basically says that when many subsets of data are expressed, the individual subsets may have specific characteristics, but expressed as one big data set they may have the opposite characteristics.\u00a0 When you don’t consider individual variables, there are pieces of the picture missing from the data set. News sources do this all the time when reporting data for state or even national and political reasons. Relating this back to the Pew article about recidivism rates, if the database reported that the recidivism rates were declining nationwide, they may be completely be eliminating the fact that they had been increasing in half of the states across the country. I like how in this source about the paradox, the author acknowledged that there needs to be treatments to fix the biased data in a good statistical model. Many of the treatments involve separating the variables into subsets or clusters. This changes the entire outcome of the data, but it is more accurate.<\/p>\n","protected":false},"excerpt":{"rendered":"

The Simpson’s Paradox is interesting when related to large corporations or news sources, because many times the paradox is manipulated in their favor. This paradox basically says that when many subsets of data are expressed, the individual subsets may have specific characteristics, but expressed as one big data set they may have the opposite characteristics.  … <\/p>\n

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