When you draw a graph- either a bar chart, a scatter plot, or even a pie chart, you have the choice of a broad range of colors that you can use. R, for example, has 657 different colors from aliceblue to yellowgreen. SAS has 13 shades of orange, 33 shades of blue, and 47 shades of green. They even have different shades of black.
You have a wealth of colors, but you can’t use all of them in the same graph. The ideal number of colors is 2.
As it has been said a picture is worth a thousand words and so it is with graphics too. 
A well constructed graph can summarize information collected from tens to hundreds or even thousands of data points. But not every graph has the same power to convey complex information clearly. (more…)
Repeated measures is one of those terms in statistics that sounds like it could apply to many design situations. In fact, it describes only one.
A repeated measures design is one where each subject is measured repeatedly over time, space, or condition on the dependent variable.
These repeated measurements on the same subject are not independent of each other. They’re clustered. They are more correlated to each other than they are to responses from other subjects. Even if both subjects are in the same condition. (more…)
The scatterplot is a simple display of the relationship between two, or sometimes three, variables. You have a wide range of options for displaying a scatterplot. In particular, you can control the location, size, shape, and color of the points in your scatterplot.
One activity in data analysis that can seem impossible is the quest to find the right analysis. I applaud the conscientiousness and integrity that underlies this quest.
The problem: in many data situations there isn’t one right analysis.
Good graphs are extremely powerful tools for communicating quantitative information clearly and accurately.
Unfortunately, many of the graphs we see today confuse, mislead, or deceive the reader.