Jesse Kloss- Jan. 24 homework
One thing I found interesting from the “How to avoid 10 common mistakes in data reporting” article was mistake number 1, which was to overestimate the meaning of a particular data set. The article mentioned that “the data is only as good as how it’s collected.” I think that is very important because I catch myself reading news articles/blogs that refer to data and trends and I have the tendency to think “oh this must be true because they have numbers” without considering how the data was collected, who collected it, and what their purpose was in doing so. An article or blog isn’t guaranteed truth because it uses numbers. The numbers can be either inaccurate, or the assumption based on the numbers could be inaccurate. It is important to make sure the trends are accurate by comparing to other data sets from different sources if the results of a certain data set seem very out of the ordinary. Thus, data collections should be taken with a grain of salt and checked thoroughly to ensure that they aren’t misleading or inaccurate. As a journalist, it is important to make sure that the data is from a reputable source so that the story isn’t misleading.
In the “AP Stylebook on Data Journalism”, I found the line “Why was the data collected? Was it for the purpose of advocacy? Might that affect the data’s reliability or completeness?” to be particularly important. If data is collected for the purpose of advocacy, it could be collected with the expectation that it will validate a certain point to support whatever issue the person is advocating. This is troubling, because that brings the integrity of the whole data collection into question, because if it was a poll, that means that the question could have been phrased in a biased way to get a certain answer, the data could have only been collected from people who would fit the advocate’s agenda, etc. It is important to understand who is collecting the data and the reasoning behind it so we can be certain that the data is unbiased as a journalist so that our stories and depiction of our data are as truthful as possible.
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