With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study. Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what’s going on in our data. Here, I concentrate on inferential statistics that are useful in experimental and quasi-experimental research design or in program outcome evaluation. Perhaps one of the simplest inferential test is used when you want to compare the average performance of two groups on a single measure to see if there is a difference. You might want to know whether eighth-grade boys and girls differ in math test scores or whether a program group differs on the outcome measure from a control group. Whenever you wish to compare the average performance between two groups you should consider the t-test for differences between groups.

Most of the major inferential statistics come from a general family of statistical models known as the General Linear Model. This includes the t-test, Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA), regression analysis, and many of the multivariate methods like factor analysis, multidimensional scaling, cluster analysis, discriminant function analysis, and so on. Given the importance of the General Linear Model, it’s a good idea for any serious social researcher to become familiar with its workings. The discussion of the General Linear Model here is very elementary and only considers the simplest straight-line model. However, it will get you familiar with the idea of the linear model and help prepare you for the more complex analyses described below. One of the keys to understanding how groups are compared is embodied in the notion of the “dummy” variable. The name doesn’t suggest that we are using variables that aren’t very smart or, even worse, that the analyst who uses them is a “dummy”! Perhaps these variables would be better described as “proxy” variables. Essentially a dummy variable is one that uses discrete numbers, usually 0 and 1, to represent different groups in your study.

Dummy variables are a simple idea that enable some pretty complicated things to happen. For instance, by including a simple dummy variable in an model, I can model two separate lines (one for each treatment group) with a single equation. To see how this works, check out the discussion on dummy variables. One of the most important analyses in program outcome evaluations involves comparing the program and non-program group on the outcome variable or variables. How we do this depends on the research design we use. research designs are divided into two major types of designs: experimental and quasi-experimental. Because the analyses differ for each, they are presented separately. Experimental Analysis. The simple two-group posttest-only randomized experiment is usually analyzed with the simple t-test or one-way ANOVA. The factorial experimental designs are usually analyzed with the Analysis of Variance (ANOVA) Model. Randomized Block Designs use a special form of ANOVA blocking model that uses dummy-coded variables to represent the blocks.

The Analysis of Covariance Experimental Design uses, not surprisingly, the Analysis of Covariance statistical model. Quasi-Experimental Analysis. The quasi-experimental designs differ from the experimental ones in that they don’t use random assignment to assign units (e.g., people) to program groups. The lack of random assignment in these designs tends to complicate their analysis considerably. For example, to analyze the Nonequivalent Groups Design (NEGD) we have to adjust the pretest scores for measurement error in what is often called a Reliability-Corrected Analysis of Covariance model. In the Regression-Discontinuity Design, we need to be especially concerned about curvilinearity and model misspecification.

Consequently, we tend to use a conservative analysis approach that is based on polynomial regression that starts by overfitting the likely true function and then reducing the model based on the results. The Regression Point Displacement Design has only a single treated unit. Nevertheless, the analysis of the RPD design is based directly on the traditional ANCOVA model. When you’ve investigated these various analytic models, you’ll see that they all come from the same family — the General Linear Model. An understanding of that model will go a long way to introducing you to the intricacies of data analysis in applied and social research contexts.

The T-Test

The t-test assesses whether the means of two groups are statistically different from each other. This analysis is appropriate whenever you want to compare the means of two groups, and especially appropriate as the analysis for the posttest-only two-group randomized experimental design.

Figure 1. Idealized distributions for treated and comparison group posttest values. | Figure 1 shows the distributions for the treated (blue) and control (green) groups in a study. Actually, the figure shows the idealized distribution — the actual distribution would usually be depicted with a histogram or bar graph. The figure indicates where the control and treatment group means are located. The question the t-test addresses is whether the means are statistically different. What does it mean to say that the averages for two groups are statistically different? Consider the three situations shown in Figure 2. The first thing to notice about the three situations is that the difference between the means is the same in all three.

But, you should also notice that the three situations don’t look the same — they tell very different stories. The top example shows a case with moderate variability of scores within each group. The second situation shows the high variability case. the third shows the case with low variability. Clearly, we would conclude that the two groups appear most different or distinct in the bottom or low-variability case. Why? Because there is relatively little overlap between the two bell-shaped curves. In the high variability case, the group difference appears least striking because the two bell-shaped distributions overlap so much.

Figure 2. Three scenarios for differences between means. |

This leads us to a very important conclusion: when we are looking at the differences between scores for two groups, we have to judge the difference between their means relative to the spread or variability of their scores. The t-test does just this. Statistical Analysis of the t-test

The formula for the t-test is a ratio. The top part of the ratio is just the difference between the two means or averages. The bottom part is a measure of the variability or dispersion of the scores. This formula is essentially another example of the signal-to-noise metaphor in research: the difference between the means is the signal that, in this case, we think our program or treatment introduced into the data; the bottom part of the formula is a measure of variability that is essentially noise that may make it harder to see the group difference. Figure 3 shows the formula for the t-test and how the numerator and denominator are related to the distributions.