Welcome to WordPress. This is your first post. Edit or delete it, then start blogging!
Welcome to WordPress. This is your first post. Edit or delete it, then start blogging!
Welcome to WordPress. This is your first post. Edit or delete it, then start blogging!
Welcome to WordPress. This is your first post. Edit or delete it, then start blogging!
Welcome to WordPress. This is your first post. Edit or delete it, then start blogging!
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.
This is my first attempt at posting, hopefully it goes well!
I think this a very interesting article that reflects important trends in recidivism in the United States. The analysis demonstrates that in some states, the percentage of incarcerated people returning to prison after three years has been decreasing. It cites the importance of new programs and re-entry policies that have benefited former inmates as one of the main reasons for this decline in recidivism. While this article makes some good points, I don’t think it can accurately reflect the United States as a whole. In certain analyses, it can be perfectly fine to use a random sample to reflect greater population. However, an analysis that identifies trends in prison statistics should warrant a more complete data collection process from each state to more accurately reflect how things are changing in our country. This is especially true because the data that is being used is not a random sample, rather it is the only data available.
Welcome to WordPress. This is your first post. Edit or delete it, then start blogging!