Teach t-tests via "Waiting to pick your baby's name raises the risk for medical mistakes"

So, I am very pro-science, but I have a soft spot in my heart for medical research that improves medical outcomes without actually requiring medicine, expensive interventions, etc. And after spending a week in the NICU with my youngest, I'm doubly fond of a way of helping the littlest and most vulnerable among us. One example of such was published in the journal Pediatrics and written up by NPR. In this case, they found that fewer mistakes are made when not-yet-named NICU babies are given more distinct rather than less distinct temporary names. The unnamed baby issues is an issue in the NICU, as babies can be born very early or under challenging circumstances, and the babies' parents aren't ready to name their kids yet. Traditionally, hospitals would use the naming convention "BabyBoy Hartnett" but several started using "JessicasBoy Hartnett" as part of this intervention. So, distinct first and last names instead of just last names. They measured patient mistakes by counting the number of Retract-and-Reorders, or how often a treatment was listed in a patient’s record, then deleted and assigned to a different patient (due to a mistake being corrected). They found that the number of retract-and-reorders decreased following the naming convention change.

This researcher DID NOT use paired t-tests their analyses. However, this research presents a good conceptual example of within-subject t-tests. As I often do around this blog, I created fake t-test data that mimicked the findings with fewer R-and-R's for doubly named babies. The data was created via Richard Lander’s data generator website:

	Before intervention	After Intervention
NICU 1	47	36
NICU 2	45	26
NICU 3	52	38
NICU 4	50	32
NICU 5	46	42
NICU 6	38	20
NICU 7	63	41
NICU 8	40	27
NICU 9	37	26
NICU 10	40	29