Diamond Plot

   for Comparing Group Means and Variability

Chong Ho Yu, Ph.D. (2015)

Comparing group differences for examining treatment effectiveness is a common practice in research and evaluation. Parametric procedures such as t-tests and F-tests are widely used for this purpose. However, those procedures are not very informative because the conclusion is nothing more than rejecting or failing to reject the null hypothesis.

APA Task Force on Statistical Inference (Wilkinson, 1996) endorsed the use of confidence intervals (CI) as a supplement to conventional p value. In hypothesis testing the p value is defined as the probability of observing the statistics at hand given the null hypothesis is true. On the other hand, use of CI does not rely on the assumption about the truth of the null.

By using CI, the researcher can look at the group differences by sample means, sample size, variability, and the estimated population means. As the sample size increases, the variability decreases, and the CI gets narrower. Why should we judge the quality of a CI by its narrowness? Take this scenario as a metaphor: You ask me to guess your age, I reply, "from 16 to 60." I am 95% confident that your actual age would fall within this range, but is it a useful estimation? Probably not. If I say "from 18-21" instead, it is definitely a much better answer.

There are at least two ways to view a CI:

  • A Fisherian who subscribes to the objective, frequentist philosophy interprets a confidence interval(CI) as "Given the choice of z = 1.96, for every 100 samples drawn, 95 of them will capture the population parameter within the bracket." According to the objective school of probability, the population parameter is constant and therefore there is one and only true value in the population.

  • However, in the view of Bayesians, the same CI can be interpreted as "given the choice of z = 1.96, the researcher is 95% confident that the population parameter is bracketed by the CI." It is important to note that in the second interpretation "confidence" becomes a subjective, psychological property. In addition, Bayesians do not treat the population parameter as a constant or true value.

SAS/JMP provides a powerful tool named diamond plot to visualize CI. The JMP tool is so easy that you don't even need to know the name of the procedure. As long as you know what your dependent and independent variables are, you can simply choose Fit Y by X from the Analyze menu, as shown in the following:

JMP provides the user with a contextual menu system and thus you would not be overwhelmed by too many options. In the next screen only the options that are applicable to the data structure are available to you. At this stage, you can select Quantiles to display the box plot and Means/Anova to display the diamond plot.

The result is shown in the following figure. It condenses a lot of important information:

  • Grand sample mean: it is represented by a horizontal black line.

  • Group means: the horizontal line inside each diamond is the group mean.

  • Confidence intervals: The diamond is the CI for each group. Because the population parameter is unknown, there is always some uncertainty in estimation. Thus, we need to bracket the estimation. Take photography as an analogy. If the photographer is not sure whether the exposure is correct, he would take at least one over-exposed photo (upper bound), one under-exposed photo (lower bound), and one in the middle. In the JMP output, the top of the diamond is the upper bound (best case scenario) while the bottom is the lower bound (worst case scenario).

  • Quantile: In addition to CI, JMP also provides the option of overlaying a boxplot showing quantile information.

In this hypothetical example, Professor Yu taught three classes in different modes: Conventional classroom, online class, and hybrid class. He wants to know which method could yield better exam scores. It is obvious that the performance gap between classroom group and the two others is significant, because the upper bound of the classroom group is close to the lower bound of the other two. However, it seems that the difference between the hybrid group and the online group is not substantive at all because there is a lot of overlapping between the two groups. If you need to report formal statistics, you can extract the appropriate information below the graphic.

When I was a graduate student, I took a course in multiple comparison procedures (MPC) as a post hoc step after ANOVA. At most the F test of ANOVA could tell you whether one of the means differ from one of the other means. In order to test which pairwise difference is significant but control the Type I error rate at the same time, different MPCs are needed. The course required the learners to memorize the pros and cons of 10-15 tests, such as LSA, Bonferroni, Ryan, Tukey, Duncan, Gabriel...etc.. To tell you the truth, today I forgot most of the information. The following is a screenshot of MPCs offered by SPSS. You can tell how confusing it is. In my opinions, the diamond pot is a much quicker and easier way for group comparison.

However, it doesn't mean that we can totally ignore post hoc multiple comparison. On some occasions it is still useful for verification when the situation is ambiguous. Take the preceding case as an example again. If we infer from the sample mean to the population mean, the best estimate of the classroom group mean is 77.22 while the worst case scenario of Hybrid is 78.676 (see the yellow highlight in the figure below). No doubt in my mind Hybrid is better than Classroom. However, the lowest estimate of Online is 75.76, which is below the best estimate of Classroom. Indeed, while the diamonds of Classroom and Hybrid do not overlap, there is a bit overlapping between the diamonds of Classroom and Online.

When we make a decision based on the CI, we can accept that a bit overlapping may still imply significance. The question is: how much is considered "a bit"? Nonetheless, if you look at the Tukey test result (one of the post hoc tests), it is obvious that both Hybrid and Online significantly outperform Classroom (p = 0.0094, p = 0.0458, respectively; see the orange and red numbers in the figure below).

JMP Tukey test

Further, Payton, Greenstone and Schenker (2003) warned researchers that inferring from non-overlapping CIs to significant mean differences is a dangerous practice, because the error rate associated with this comparison is quite large. The probability of overlap is a function of the standard error. As the standard errors become less homogeneous, the probability of overlap decreases. Simulations result showed that when the standard errors are approximately equal, using 83% or 84% size for the intervals will give an approximate alpha = 0.05 test, but using 95% confidence intervals, which is a common practice, will give very conservative results. Thus, researchers are encouraged to use both CI and hypothesis testing.


Payton, M. E., Greenstone, M. H., & Schenker, N. (2003). Overlapping confidence intervals or standard error intervals: What do they mean statistical significance? Journal of Insect Science, 3(34). Retrieved from http://insectscience.org/3.34

Wilkinson, L, & the task Force on Statistical Inference. (1996). Statistical methods in psychology journals: Guidelines and explanations. Retrieved from http://www.apa.org/science/leadership/bsa/statistical/tfsi-followup-report.pdf

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