Examining patient from a urologist implies Berkson Bias which would skew the population mean of serum urea nitrogen away from the true accurate mean. Then, realize precision is dependent on statistical "Power" which is increased based on the size of the population of the study. (increased precision = increased statistical power). Therefore, an increase in population of a biased group with lead to inaccuracy with high precision.

1. Example of inaccurate but highly precise 
    a. 500 patients seeing a particular doctor for a particular illness
2. Example of accurate but imprecise
    a. 10 patients undergo a screening at a mall 
3. Both Accurate and precise 
    a. 500 patients (high precision) undergo a screening (high accuracy ~ no bias or systemic error)

Accuracy means the data points are dispersed, but when you take the mean of those points, that mean (“sample mean”) is nearby the population mean (“true mean”). Data points are “more precise” if the dispersion across data points is smaller than some other set of data points (notice how this is a comparison and not an “absolute” statement); precision says nothing about how close the average of the data points are to the “true mean.”

Keep in mind that accuracy and precision are relative descriptors; you can’t say “so-and-so is precise”; no, you can only say “such-and-such is more precise than so-and-so” or “so-and-so is more accurate than such-and-such.” So, in this case, we can infer that NBME considers “men at the urologist” to have BUNs that are closer to each other (more clustered; more precise; less dispersed) than the BUNs of “men at mall.”

Here’s a nice image:
https://medbullets.com/images/precision-vs-accuracy.jpg

Discussing precision only makes sense if they were to sample "X # patients" multiple times and see how close the different measurements' results were to each other. The actual size of the sample should't affect precision, but rather it should just affect accuracy (which is reduced by the biased population at the urologist). Smh