For example, a question that is of current interest on campus is, "Are
students willing to buy a computer which will be paid for by an increase
in tuition?" The population here is all students at WMU. Now if we knew
the population we would know how many students (what proportion) are willing
to have an increase in tuition to offset the purchase of a computer. And
this could be done, but it will take time (read as MONEY) to gather this
information.

In an attempt to answer this question we obtain a random sample from the population. By random sample (here we go again) we mean

- 1.
**The items in the sample are independent of one another.**- 2.
**Conditions do not change as the sample is gathered.**

Another question along these lines is: "What's the family income of
a student (or more specifically, the income paying the student's tuition)?"
We mean of course the population distribution of the family incomes of
all the students. We could use our sample to estimate this. Actually the
histogram of the family incomes of the sample students is our estimate
of the population distribution of the family incomes of all the students.
From working with histograms, though, you know that we need quite a large
sample to estimate this population distribution accurately. We could rephrase
the question as: "What's the **average **family income of a student
(or more specifically, the **average **income paying the student's tuition)
?" Then our sample average would be an estimate of the population average.
Again, the question we must answer is: **How much did our estimate miss
by?** We need an estimate of error of estimation.