I’m starting the process of drafting a report to summarize my in-depth analysis of the police shooting dataset compiled by the Washington Post. As the initial step, I’m exploring the data and formulating potential questions and hypotheses that will guide my investigative report.
My report will focus on examining issues like racial disparities, use of force policies, mental illness, and geographic trends. Developing meaningful questions is crucial before applying statistical tests and machine learning techniques to derive insights.
For example, my report will probe questions like: Does victim race remain a significant predictor of shooting likelihood when accounting for other factors? Have fatal shootings increased over time even when adjusted for population changes? What policy and training reforms does the data suggest could reduce shootings?
I’m compiling a list of probing questions on the intricacies and nuances of police lethal force usage to thoroughly structure my report. My goal is to leverage the right analytical tools to extract compelling data-driven discoveries and conclusions from this rich dataset.
The report will document my process of moving from initial data exploration to formal statistical analysis. I’ll detail the hypotheses tested, models built, and key findings on where policing reform is most needed based on the data.
This brainstorming phase is essential for focusing my analysis before I begin writing. I’m eager to finalize my investigatory plan and begin deriving impactful insights to include in a comprehensive report on Washington Post’s police shooting database. My goal is contributing meaningful findings to this important issue.