A t-test is a statistical hypothesis test used to determine if there’s a significant difference between the means of two groups or datasets. In the context of our dataset on police shootings, we can apply a t-test to compare specific characteristics of two groups and assess whether the observed differences are statistically significant.
For example, if we want to investigate whether there’s a significant difference in the ages of individuals involved in police shootings based on gender (male and female). To do this, we must first separate our dataset into these two groups. Then, we can apply a t-test to evaluate if the age means of these groups differ significantly. The null hypothesis in this case would be that there’s no significant age difference between the two gender groups, and the alternative hypothesis would suggest that there is a significant difference.
By performing a t-test, we can quantify the extent of the difference and determine whether it’s likely due to random chance or if it represents a meaningful distinction between the two groups. This allows us to draw statistical conclusions about the impact of gender on the ages of individuals involved in police shootings, providing valuable insights based on the characteristics of our dataset.