The Washington Post’s database on fatal police shootings provides extensive data that can be analyzed using statistical techniques like p-tests. In this post, I’ll overview how p-tests could help extract insights from this dataset.P-tests are used to determine if an observed difference between groups is statistically significant or likely due to chance. Some ways p-tests could be applied include:
– Testing racial disparities in shooting rates per capita between groups like Black vs. White victims. A significant p-value could confirm real differences exist.- Comparing the armed status of victims across situational factors like fleeing, mental illness, location. P-tests can identify significant interactions.- Analyzing trends over time. Are increases/decreases in quarterly shooting rates year-to-year significant based on p-values?- Assessing differences in victim mean age by race. Low p-values would demonstrate age gaps are meaningful.
By setting a significance level (often 0.05) and calculating p-values, researchers can make statistical conclusions on observed differences. Significant p-values reject the null hypothesis of no real difference between groups.P-testing provides a straightforward method to make rigorous statistical evaluations using the Washington Post data. It moves beyond simple descriptions to formally test hypotheses on factors like race, mental illness, armed status, age, location and make data-driven conclusions. This allows deeper understanding of police shooting causal patterns.