On a card putting the cards in an urn and shuffling

on a card, putting the cards in an urn, and shuffling the urn. Then, pull out cards one by one and
set them aside, stopping when the specified sample size is reached.
We’ve produced two samples of the salary_data table in this way:
small_srswor_salary.csv and large_srswor_salary.csv contain, respectively, a sample
of size 44 (the same as the convenience sample) and a larger sample of size 100.
The load_data function below loads a salary table and joins it with player_data .
Question 3.6 Run the same analyses on the small and large samples that you previously ran on
the full dataset and on the convenience sample. Which is more accurate, the estimate of population
statistics that we get from the convenience sample, the estimate from the small simple random
sample, or the estimate from 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.)
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In [42]: _ = ok . grade( ' q3_6 ' )
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Running tests
---------------------------------------------------------------------
Test summary
Passed: 4
Failed: 0
[ooooooooook] 100.0% passed
1.3.3 Producing simple random samples
Often it’s useful to take random samples even when we have a larger dataset available. Another
is to help us understand how inaccurate other samples are.
Tables provide the method sample() for producing random samples. Note that its default
is to sample with replacement. To see how to call sample() , search the documentation on the
datascience documentation of the course website, or enter full_data.sample? into a code cell
and press Shift + Enter.
Question 3.7 Produce a simple random sample of size 44 from full_data . (You don’t need
to bother with a join this time -- just use full_data.sample(...) directly. That will have the
same result as sampling from salary_data and joining with player_data .) Run your analysis on
it again.
- Are your results roughly similar to those in the small sample we provided you? Run your code
several times to get new samples.
- How much does the average age change across samples? - What about average salary?
Out[43]: array([2.69318182e+01, 4.42913811e+06])
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Although the results are similar, they are not the same as the sample we were given. The
average age tends to stay around the same value since there is a limited range of ages for NBA
players, but the salary changes by a significant factor due to larger variability in salary.