Population, sample, and sampling

Understanding the population and sample and what are their differences.

To make a statement about something, you must always specify the scope. The scope is the characteristics of people, animals, cells, object, or situation that your claim may be applied to. Your study will be focused on that extent: include that group of interest into the experiment and exclude others out. Without such scope is the generalization and may put you at risk of overclaiming.

Population

In the study design, population is the largest group that includes all people, animal, cell, or thing within your scope. In other words, population refers to everybody or everything that you want to study. Your result should be applicable to them and may not for others.

Inclusion and exclusion criteria

You have two methods of specifying your population: by inclusion and by exclusion. Those specification criteria will efficiently remind you, your colleagues, and readers about the definitive scope that your study focused on.

Inclusion criteria

First, you should include people, animal, cell, or thing that you interested in. For example, when you want to study about the diseases found in elderly, you may want to include people with age of 65 years old or more. If you want to see how your treatment works among those who have some specific disease, you may want to limit the inclusion criteria to people with that disease.

Please remember that inclusion criteria should be meant to include. Although the specification to some characteristic is allowed, good inclusion criteria must not have the "NO" words.

Exclusion criteria

After the inclusion, you may think of which characteristics that may interfere or unwanted in your study. Exclusion of those could be beneficial and/or make your study less complicate. For instance, people who undergoing regular blood transfusion (receiving blood) should not yet included in the clinical trial for proposing new medication.

If you have specified the group by the inclusion criteria, you don't have to state that you exclude people that did not meet the inclusion requirement again.

Parameters: the measurement of population

In most of the studies, you want to know (or at least estimate) the real measurement of the whole population such as decrease in glucose level when people took drugs for diabetes, life expectancy (age of death) of people with neurodegenerative diseases. These measurements are called parameters.

Parameter is the characteristic value of the population. With this fact and some knowledge, you may be able to determine what they behave or outcome of the intervention you give to them precisely. This is the precious information that every researcher hardly works on it. However, this parameter is the only-God-known quantity. You have to collect data from everyone and everything within those criteria without any mistake, which is time-consuming and ideal even for the small population.

Here is what statistics come to rescue. Statisticians has proved with math that, with only partial study on some people or things, you can reach that seemingly inestimable "parameter" with some error. That is the origin of sample and the sampling method.

Sample and sampling methods