Evitt Nashed
Dr. Smith
March 25, 2024
After synthesizing and reading two articles about cleaning data, I feel as if cleaning the data is more ethical when doing research. I didn’t agree with the article, “Against Cleaning” because data cleaning can help you have better visualizations and useful data. The author talked about how Data cleaning can cause a messy outcome for their research. I honestly fully disagree with that. Being uncertain causes messes within the research. It’s good to review your data more than once and correct the errors.
An article I agreed with was the article about data visualizations. This article went into depth about the ways we can make uncertainty disappear. It gives us examples like refining the data you’ve made. The author’s main point in this article is to face ambiguity head-on through visual means. Another example to fix uncertainty would be plotting data with statistical, visual displays.
One way we can deal with complicated data is to show your workflow of how you are forming that data. That workflow can help make understanding data a lot easier. Another example of dealing with complicated data would be of course cleaning the data. I believe that not cleaning your data can cause a load of messy work and uncertainty. The last example I feel is important when dealing with complicated data is to keep the original data raw and to make a copy of it and work on that copy. It is important to keep your raw information so that other researchers can look back into that raw data.
To conclude, I agree with cleaning your data so that it is more efficient to look back on. Not cleaning your data can cause problems within the data which can make it a lot harder to work with. I can use this information in the future by always looking over my data visualizations and making sure it’s clean and makes sense.