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12.1: Observing and Interpreting Variability

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    We have seen time and again that scores, be they individual data or group means, will differ naturally. Sometimes this is due to random chance, and other times it is due to actual differences. Our job as scientists, researchers, and data analysts is to determine if the observed differences are systematic and meaningful (via a hypothesis test) and, if so, what is causing those differences. Through this, it becomes clear that, although we are usually interested in the mean or average score, it is the variability in the scores that is key.

    Take a look at Figure \(\PageIndex{1}\), which shows scores for many people on a test of skill used as part of a job application. The \(x\)-axis has each individual person, in no particular order, and the \(y\)-axis contains the score each person received on the test. As we can see, the job applicants differed quite a bit in their performance, and understanding why that is the case would be extremely useful information. However, there’s no interpretable pattern in the data, especially because we only have information on the test, not on any other variable (remember that the x-axis here only shows individual people and is not ordered or interpretable).

    fig 11.1.1.png
    Figure \(\PageIndex{1}\): Scores on a job test

    Our goal is to explain this variability that we are seeing in the dataset. Let’s assume that as part of the job application procedure we also collected data on the highest degree each applicant earned. With knowledge of what the job requires, we could sort our applicants into three groups: those applicants who have a college degree related to the job, those applicants who have a college degree that is not related to the job, and those applicants who did not earn a college degree. This is a common way that job applicants are sorted, and we can use ANOVA to test if these groups are actually different. Figure \(\PageIndex{2}\) presents the same job applicant scores, but now they are color coded by group membership (i.e. which group they belong in). Now that we can differentiate between applicants this way, a pattern starts to emerge: those applicants with a relevant degree (coded red) tend to be near the top, those applicants with no college degree (coded black) tend to be near the bottom, and the applicants with an unrelated degree (coded green) tend to fall into the middle. However, even within these groups, there is still some variability, as shown in Figure \(\PageIndex{2}\).

    fig 11.1.2.png
    Figure \(\PageIndex{2}\): Applicant scores coded by degree earned

    This pattern is even easier to see when the applicants are sorted and organized into their respective groups, as shown in Figure \(\PageIndex{3}\).

    fig 11.1.3.png
    Figure \(\PageIndex{3}\): Applicant scores by group

    Now that we have our data visualized into an easily interpretable format, we can clearly see that our applicants’ scores differ largely along group lines. Those applicants who do not have a college degree received the lowest scores, those who had a degree relevant to the job received the highest scores, and those who did have a degree but one that is not related to the job tended to fall somewhere in the middle. Thus, we have systematic variance between our groups.

    We can also clearly see that within each group, our applicants’ scores differed from one another. Those applicants without a degree tended to score very similarly, since the scores are clustered close together. Our group of applicants with relevant degrees varied a little but more than that, and our group of applicants with unrelated degrees varied quite a bit. It may be that there are other factors that cause the observed score differences within each group, or they could just be due to random chance. Because we do not have any other explanatory data in our dataset, the variability we observe within our groups is considered random error, with any deviations between a person and that person’s group mean caused only by chance. Thus, we have unsystematic (random) variance within our groups.

    The process and analyses used in ANOVA will take these two sources of variance (systematic variance between groups and random error within groups, or how much groups differ from each other and how much people differ within each group) and compare them to one another to determine if the groups have any explanatory value in our outcome variable. By doing this, we will test for statistically significant differences between the group means, just like we did for \(t\)-tests. We will go step by step to break down the math to see how ANOVA actually works.

    This page titled 12.1: Observing and Interpreting Variability is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Foster et al. (University of Missouri’s Affordable and Open Access Educational Resources Initiative) via source content that was edited to the style and standards of the LibreTexts platform.

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