Skip to main content
Social Sci LibreTexts

7.1: Populations Versus Samples

  • Page ID
    • Anonymous
    • LibreTexts
    \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \) \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)\(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\) \(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\)\(\newcommand{\AA}{\unicode[.8,0]{x212B}}\)

    Learning Objectives

    • Understand the difference between populations and samples.

    Who or What?

    When I teach research methods, my students are sometimes disheartened to discover that the research projects they complete during the course will not make it possible for them to make sweeping claims about “all” of whomever it is that they’re interested in studying. What they fail to realize, however, is that they are not alone. One of the most surprising and frustrating lessons research methods students learn is that there is a difference between one’s population of interest and one’s study sample. While there are certainly exceptions, more often than not a researcher’s population and her or his sample are not the same.

    In social scientific research, a population is the cluster of people, events, things, or other phenomena that you are most interested in; it is often the “who” or “what” that you want to be able to say something about at the end of your study. Populations in research may be rather large, such as “the American people,” but they are more typically a little less vague than that. For example, a large study for which the population of interest really is the American people will likely specify which American people, such as adults over the age of 18 or citizens or legal residents. A sample, on the other hand, is the cluster of people or events, for example, from or about which you will actually gather data. Some sampling strategies allow researchers to make claims about populations that are much larger than their actually sample with a fair amount of confidence. Other sampling strategies are designed to allow researchers to make theoretical contributions rather than to make sweeping claims about large populations. We’ll discuss both types of strategies later in this chapter.

    As I’ve now said a couple of times, it is quite rare for a researcher to gather data from their entire population of interest. This might sound surprising or disappointing until you think about the kinds of research questions that sociologists typically ask. For example, let’s say we wish to answer the following research question: “How do men’s and women’s college experiences differ, and how are they similar?” Would you expect to be able to collect data from all college students across all nations from all historical time periods? Unless you plan to make answering this research question your entire life’s work (and then some), I’m guessing your answer is a resounding no way. So what to do? Does not having the time or resources to gather data from every single person of interest mean having to give up your research interest? Absolutely not. It just means having to make some hard choices about sampling, and then being honest with yourself and your readers about the limitations of your study based on the sample from whom you were able to actually collect data.

    Sampling is the process of selecting observations that will be analyzed for research purposes. Both qualitative and quantitative researchers use sampling techniques to help them identify the what or whom from which they will collect their observations. Because the goals of qualitative and quantitative research differ, however, so, too, do the sampling procedures of the researchers employing these methods. First, we examine sampling types and techniques used in qualitative research. After that, we’ll look at how sampling typically works in quantitative research.


    • A population is the group that is the main focus of a researcher’s interest; a sample is the group from whom the researcher actually collects data.
    • Populations and samples might be one and the same, but more often they are not.
    • Sampling involves selecting the observations that you will analyze.


    1. Read through the methods section of a couple of scholarly articles describing empirical research. How do the authors talk about their populations and samples, if at all? What do the articles’ abstracts suggest in terms of whom conclusions are being drawn about?
    2. Think of a research project you have envisioned conducting as you’ve read this text. Would your population and sample be one and the same, or would they differ somehow? Explain.

    This page titled 7.1: Populations Versus Samples is shared under a CC BY-NC-SA 3.0 license and was authored, remixed, and/or curated by Anonymous via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.