Statistics Everywhere

My cousin, a middle school mathematics teacher, wouldmotivate her students by reminding them that “math is everywhere.” This is clearly true in hematology. We use blood counts, SUVs from PET imaging, and quantitative assessments of minimal residual disease to assess tumor burden and define the need for further therapy. Laboratory studies report percentage of viable cells after exposure to various agents. Mice are subjected to serial bioluminescent imaging to assess whether a novel agent can impact the trajectory of tumor growth. In such a quantitative environment, we rely on statistics to help us process and interpret our abundance of data. The need for statistical assistance in the design and analysis of clinical, laboratory, and animal experiments in hematology seems to elicit a high level of anxiety in many investigators. Statisticians, bioinformaticians, and computational biologists are available to assist researchers in most academic institutions—universities as well as hospitals—but many of these individuals are focused on theoretical rather than collaborative research. Relatively few of these individuals are actually embedded in hematology, and therefore familiar with leukemia, lymphoma, multiple myeloma, or the many varieties of nonmalignant hematology. Moreover, these individuals are often judged academically on their theoretical research, with little or no acknowledgment of their value in promoting statistical thinking among clinical and basic scientists. We will not focus here on the generation of P values. We will focus instead on the importance of statistical thinking—a quantitative aspect of critical thinking—in the design of scientific investigations, and the role of statistical analysis in interpreting the data collected through such investigations. The statistical design of experiments is exceedingly important to assure that a study is of an adequate size to identify as statistically significant a result of scientific importance. It is crucial to distinguish between the hypothesized difference of scientific importance and the observed data generated by the