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Current time:0:00Total duration:2:51

AP.STATS:

UNC‑3 (EU)

, UNC‑3.H (LO)

, UNC‑3.H.5 (EK)

, UNC‑3.I (LO)

, UNC‑3.I.1 (EK)

- [Instructor] We're told
Alejandro was curious if sample median was an unbiased estimator of population median. He placed ping pong balls
numbered from zero to 32 so I guess that would be
what, 33 ping pong balls in a drum and mixed them well. Note that the median of the
population is 16, alright? The median number of course
yes in that population is 16. He then took a random sample of five balls and calculated the median of the sample. So we have this population of balls. He takes a, we know the
population parameter. We know that the population median is 16 but then he starts taking
a sample of five balls so n equals five and he
calculates a sample median, sample median, and then he replaced the balls
and repeated this process for a total of 50 trials. His results are summarized
in the dot plot below where each dot represents
the sample median from a sample of five balls. So he does this, he
takes these five balls, puts them back in then he does it again then he does it again and
every time he calculates the sample median for that sample and he plots that on the dot plot so, and he will do this for 50 samples and each dot here represents
that sample statistic so it shows that four times
we got a sample median, in four of those 50 samples,
we got a sample median of 20. In five of those sample medians, we got a sample median of 10 and so what he ends up
creating with these dots is really an approximation
of the sampling distribution of the sample medians. Now, to judge whether it is a
biased or unbiased estimator for the population median, well, actually, pause the video, see if you can figure that out. Alright, now let's do this together. Now, to judge it, let's think about where the
true population parameter is, the population median. It's 16, we know that and
so that is right over here, the true population parameter. So if we were dealing with a biased, a biased estimator for
the population parameter then as we get that, our approximation of the
sampling distribution, we would expect it to be somewhat skewed. So for example, if the sampling, if this approximation of
the sampling distribution looked something like that then we'd say, okay, that looks like a biased estimator or if it was looking something like that, we'd say, okay, that looks
like a biased estimator but if this approximation
for our sampling distribution that Alejandro was constructing where we see that roughly
the same proportion of the sample statistics came out below as came out above the true parameter and it doesn't have to be exact but it seems roughly the case,
this seems pretty unbiased and so to answer the question
based on these results, it does appear that the sample median is an unbiased estimator
of the population median.

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