Through Tukey's work, and that of others, data analysis that follows the paradigms of scientific statistics has been called Exploratory Data Analysis (EDA). Recently, EDA and the statistical graphics that often accompany it have emerged as important themes of computer-based statistical data analysis. An important aspect of these advances is that, contrary to traditional methods, they do not require data from designed experiments or random samples; they can work with serendipitous data. By examining the differences in these two philosophies of statistical data analysis, we can see important trends in the future of statistics software.

All statistical data analyses work with models or descriptions of the data and the data's relationship to the world. Functional models describe patterns and relationships among variables. Stochastic models try to account for randomness and error in the data in terms of probabilities, and provide a basis for inference. Traditional statistical analyses work with a specified functional model and an assumed stochastic model. Exploratory methods examine and refine the functional model based on the data, and are designed to work regardless of the stochastic model.

Statistics software has traditionally supported mathematical statistics. The data analyst is expected to specify the functional model before the analysis can proceed. (Indeed, that specification usually identifies the appropriate computing module.) The stochastic model is assumed by the choice of analysis and testing methods. Statistics packages that support these analyses offer a large battery of tests. They avoid overwhelming the typical user because a typical path through the dense thicket of choices is itself relatively simple.

The many branches in this design (Figure 1) encourage modularity, which in turn encourages a diversity of alternative tests and the growth of large, versatile packages. Most of the conclusions drawn from the analysis derive from the hypothesis tests. Printing (in Figure 1) includes plotting, but simply adding graphics modules to such a program cannot turn it into a suitable platform for EDA. For that we need a different philosophy of data analysis software design.

Software to support scientific statistics must support exploration of the functional model and be forgiving of weak knowledge of the stochastic model. It must thus provide many plots and displays, offer flexible data management, be highly interconnected, and depend on methods other than traditional hypothesis tests to reveal data structure. A schematic might look like Figure 2. Note that there is no exit from this diagram. Most of the paths are bi-directional, and many are omitted. The data analyst learns about the data from the process of analysis rather than from the ultimate hypothesis test. Indeed, there may never be a hypothesis test.

Software to support scientific statistics is typically not modular because each module must communicate with all the others. The complexity can grow factorially. It is thus harder to add new capabilities to programs designed for scientific statistics because they cannot simply plug in as new modules. However, additions that are carefully designed benefit from the synergy of all capabilities working together.

The user interface of such software is particularly important because the data analyst must “live” in the computing environment while exploring the data and refining the



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