I looked at the ages of people from different racial backgrounds who lost their lives in police incidents. We categorized the races as Asian (A), Black (B), Hispanic (H), Native American (N), Other (O), and White (W) and calculated statistical parameters such as median, mean, standard deviation, variance, skewness, and kurtosis for the ages of individuals from each racial category using Python.
The code snippet is as follows:
When the code is implemented, I got the descrptive statistics of ages of all races. In this update I am also going to explain about Bayes’ theorem.
Bayes’ theorem is a fundamental concept in probability theory and statistics that helps us update our beliefs about an event based on new evidence or information. It’s like a math formula that shows how to adjust our understanding when we get new data.
In simple terms, think of it as a way to figure out the probability of something happening, given what we already know and some new facts we’ve learned. It’s particularly useful in situations where we want to make predictions or decisions based on incomplete or uncertain information. Bayes’ theorem helps us blend our prior knowledge with new observations to arrive at more accurate conclusions. We used this in our data by taking a null hypothesis “The mean age of black people shot by police is equal to the mean age of white people shot by police.” and applied Bayes’ theorem to it.