Suppose you survey only
relatively new
players with ages less than 22. (The more experienced players didn't bother to answer your surveys about their salaries.)
Question 2.3
Assign
convenience_sample_data
to a subset of
full_data
that contains only the rows for players under the age of 22.
In [ ]:
convenience_sample = full_data.where("Age", are.below(22))
#SOLUTION
convenience_sample
In [ ]:
_ = ok.grade('q2_3')

Question 2.4
Assign
convenience_stats
to a list of the average age and average salary of your convenience sample, using the
compute_statistics
function.
.

Question 2.5
Does the convenience sample give us an accurate picture of the age and salary of the full population of NBA players in 2014-2015? Would you expect it to,
in general? Before you move on, write a short answer in English below. You can refer to the statistics calculated above or perform your own analysis.
.

Question 2.6
Run the same analyses on the small and large samples that you previously ran on the full dataset and on the convenience sample. Compare the accuracy
of the estimates of the population statistics that we get from the convenience sample, the small simple random sample, and the large simple random sample. (Just notice
this for yourself -- the autograder will check your sample statistics but will not validate whatever you do to compare.)