It’s one thing to study a graph, but you can really understand the nuance and complexity of the data when you manipulate it yourself! See if you can use the NYPD Bar Chart App to recreate Figure 1A and Figure 1B on your own. Then modify the graph to answer the questions below.
1. Before we make any conclusions about a graph or dataset, it is important to ask critical questions to determine if the data is trustworthy. How would you evaluate the data in the NYPD Bar Chart App?
a) What is the source?
- Where is this data coming from?
- What is the purpose of this information? Would this source have any desire to influence how people feel about this issue?
- How was the data collected? It is reasonable to assume that the data was accurately recorded?
- Does this data agree with other sources?
b) What’s the context?
- What measurements are we most interested in? Is it reasonable to assume that the available data can be used to address our questions?
- Are the numbers saying something about an entire population or just a restricted subset of a population?
- When was the data collected? Does the timing of the data collection restrict what we can conclude with this data?
- Is there any missing data, missing context, or missing information that we need to consider?
- What do other studies show?
c) What assumptions are we making? It can be very easy to produce biased results even with reliable data.
- How can we be sure that we are not simply using the data to support what we want to be true? Are we incorporating some of our own personal assumptions when drawing conclusions from this data?
1D: Get Curious
2. Which graph should be used to better understand the possible patterns of discrimination in the NYPD, Figure 1A, 1B, or both? Briefly describe how each graph can contribute to addressing Focus Question 1. How does the story change if both graphs are used? 3. Why is it important to consider the racial distribution of the entire city when looking at these graphs?4. When is it important to look at multiple graphs before drawing conclusions from a dataset?5. In each report, a suspect is identified by the police as male, female, or unknown. Are there any clear patterns related to the gender of the suspect? Assuming a male was stopped, is he more likely to be arrested than a female? Do these patterns hold true across races? (Hint: try faceting by race.)6. Which crime type tends to have the most arrests each year?7. Develop your own question that could be answered with the above NYPD Bar Chart app. Write a one paragraph answer to your question.
a) Assume your audience already understands the source and context of the data.
b) Include one or two graphs (cut and pasted from the app above).
c) Clearly state your question, describe the variables in the graph(s), interpret your graph(s), and discuss what conclusions you are able to draw from these graphs.