In his article “How to Talk Back to a Statistics” Huff’s data sources come by analyzing documents such as journals, magazines and newspapers as an example to educate individuals in ascertaining proper statistical findings. For example the Chicago Journal of Commerce that made a survey on corporations’ price gorging and hoarding “You may read a proud announcement by the Chicago Journal of Commerce” (John, 2014, p. 62). He also uses magazines like Look and New Yorker magazines “An article in Look Magazine says” and “Here is an extract from a New Yorker magazine” (John, 2014, p.63). He also gets his data through personal reports when he quotes a statement from someone about John Hopkins University admitting women, “someone not particularly enamored
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Since the author is mainly reviewing documents, it is difficult to tell the sample size. Though the author has used the aggregated macro data collection as a means of coming up with his conclusion. This is so, as his findings come from data that has been collected at a micro level. For example the findings from Cornell University, “please note that while the data came from Cornell, the conclusions were entirely the writer’s own” (John, 2014, p.62).
The author has succeeded in educating people to question statistical reports before considering the findings to be true. According to him it is important to ask questions like: who says so, how does he know, what’s missing, did somebody change the subject and does it make sense. I find it important to always note conscious bias in any statistical conclusions. This is so as findings can be as an outcome of using favorable or suppressing of unfavorable data. I am in agreement with the author as people tend to lean on what favors them to come up with statistical conclusions. For example during the elections a channel can bring in poll results of their favorite candidate, showing how they are doing well as compared to their competitors but in the reality those are just individual findings there is no scientific research. We are never shown how the data is collected, the sample size, who was considered and how long the data took to be
Since the author is mainly reviewing documents, it is difficult to tell the sample size. Though the author has used the aggregated macro data collection as a means of coming up with his conclusion. This is so, as his findings come from data that has been collected at a micro level. For example the findings from Cornell University, “please note that while the data came from Cornell, the conclusions were entirely the writer’s own” (John, 2014, p.62).
The author has succeeded in educating people to question statistical reports before considering the findings to be true. According to him it is important to ask questions like: who says so, how does he know, what’s missing, did somebody change the subject and does it make sense. I find it important to always note conscious bias in any statistical conclusions. This is so as findings can be as an outcome of using favorable or suppressing of unfavorable data. I am in agreement with the author as people tend to lean on what favors them to come up with statistical conclusions. For example during the elections a channel can bring in poll results of their favorite candidate, showing how they are doing well as compared to their competitors but in the reality those are just individual findings there is no scientific research. We are never shown how the data is collected, the sample size, who was considered and how long the data took to be