Statistics in Science
No shortcuts when collaborating
This column is written for statisticians with master’s degrees and highlights areas of employment that will benefit statisticians at the master’s level. Comments and suggestions should be sent to Keith Crank, the ASA’s research and graduate education manager, at firstname.lastname@example.org.
Shari Messinger is an associate professor of biostatistics and director of the Biostatistics Collaboration and Consulting Core at the University of Miami Miller School of Medicine. She formerly served as director of biostatistics for the University of Miami GCRC and biostatistics director for the Diabetes Research Institute.
As a collaborating statistician, I am often asked by researchers to “run their data” so they can get the answers they seek corresponding to a particular investigation. What they are really requesting (usually) is that I perform data analysis to address their research questions—some of them quite vague—and that I first need to determine an appropriate analytic approach based on the nature of the investigation, study design, distributional properties of the data, and particular research objectives. Although I know this is really what they are requesting, I am not sure if they are really aware of what is involved.
I currently direct an academic biostatistical consulting core that charges an hourly rate. The following is an edited excerpt from an email I received regarding a bill sent following an analysis of two data sets:
This illustrates a common misunderstanding as to what statistical support is and clearly expresses the belief that statistical support is merely the “labor” of plugging data into a computer and pushing a button. Too many researchers regard statistics as a useful tool, but they think there is a single, straightforward way to address any particular question and that no sound judgment or creativity is required to achieve excellence. One may think of this like needing a Phillips screwdriver with a specific head size and going to the tool box to pick the right one. However, statistical analysis is not this way. It is the application of scientific methods—statistical methods, specifically—to the research objectives at hand. We statisticians must continually educate our subject-matter collaborators as to what statistical science really is.
Statistical science is much more than data analysis, and involves the incorporation of statistical methodology at all stages of research, requiring scientific expertise in the field of statistics. Appropriate use of statistical methodology in data analysis means the data should be analyzed in a way that is both scientifically and statistically reasonable. The statisticians are, themselves, scientists collaborating in research, and are using their statistical expertise in determining and applying the appropriate methodology for rigorously addressing important research questions with excellence. The time invested often requires the following:
- Review of the research for basic understanding of the science
- Review of the data to understand the distributional properties of the variables collected
- Determination of the appropriate methodology to apply in analysis corresponding to the hypothesis and design of the investigation
- Programming of the analysis using appropriate statistical software (specific to the particular data set)
- Review of the analytic results
- Reporting of the results
The time invested for a particular data analysis can take hours, or it can take months. This depends on the research questions, the study design, the properties of data gathered, and the target audience that will need to understand the results.
As collaborating statisticians, it is up to us to educate the research community, making them aware that our contributions involve the incorporation of statistical science to their research. We can do this by communicating with them and explaining what goes into providing high-quality collaborative statistical science that supports outstanding research programs. We must interact with the research team as collaborators, discussing the statistical issues of all aspects of the investigation. Everyone wins when the whole team understands how to fully incorporate statistical science into the entire research process.
I replied to Dr. Doe’s email by making these points, almost verbatim. His misunderstanding is common, especially for newer investigators. Thankfully, most of them take kindly to learning about statistical science and this improves their research. What we do is important; there are no shortcuts.