IIt can be appropriate to represent the population with a sample, although the validity of the
results depends on how accurately the sample reflects the population. Per the textbook
(Illowsky & Dean, 2013), a sample should have the same qualities as the population it is
representing. If I wanted to know the average GPA of engineering students I would not only
select participants from honors classes since that would create a bias and I would have
inaccurate results that do not necessarily reflect the GPAs of engineering students as a
whole. Instead, if I wanted to appropriately represent the engineering student body, I would
want to randomly select students from varying engineering majors. It is also important to
consider the size of the sample group when representing the population since too large or
too small of a sample group may lead to biased or invalid results. In general, larger samples
are considered to be better (Illowsky & Dean, 2013), although larger samples can highlight
biases within that group. For example, when a restaurant receives reviews it is more likely
that customers who had extremely wonderful or horrible experiences that go out of their way
to leave a review. This sample group causes biased and polarized results that may not
accurately represent the population. A sample size that is too small may also lead to flawed
results. If a professor wanted to see which subject the students were struggling on based on
incorrect answers from the previous exam and the professor were to select 4 students'
exams, it is possible that the results would be inconclusive since it could be that the 4
students struggled on different subjects. In order to appropriately represent a population by
a sample, it is important to limit sampling bias and find a balanced sample size.
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