This is the first of a three-part installment on sampling and inferences made from sampling.

As the Presidential race kicks into full gear (we hope it does anyway) we will see more and more polls come out as we get closer to election time.

There is much debate about polls, even among experts.

But to the non-expert, allow me to explain a few things.

First, a few definitions. A* population* is every item (or in many cases, person) of interest to a researcher. For example, if a person is interested in the average age of *all* people who go into a store one day (and is only interested in that particular day), then the population would be *all* people that walk into the store that day.

If, for example, 100 people walked through that door on that day, then the 100 people would represent the population. However, the population could also be defined as the 100 *ages* of the 100 people. In other words, either the ages or the people themselves can be considered the population.

The population can be defined in any way the investigator wants. It will normally be dependent upon his interest.

The *sample* is a subset of the population. In the example above, if you determined the ages of, say, the first 10 people in the store, then the sample would be those 10 people (or the 10 ages).

Perhaps the biggest factor that investigators want to focus on is something called *bias*. Perhaps I should say the *biggest pitfall one wants to avoid* is that of bias, as bias can completely distort your findings. Bias occurs when a sample does not *represent* the population.

Perhaps the most famous case of bias in sampling came in 1948.

We will talk more about bias (along with types of sampling) in the second part.

The third part will focus more on this year’s Presidential election (and a look back at the 2016 election).

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