Undergraduate students who have just completed an introductory statistics course often lack deep understanding of variability and enthusiasm for the field of statistics. This paper argues that by introducing the commonly underemphasized concept of measurement error, students will have a better chance of attaining both. We further present lecture materials and activities that introduce metrology, the science of measurement, which were developed and tested in a pilot study at Iowa State University. These materials explain how to characterize sources of variability in a dataset, in a way that is natural and accessible because the sources of variability are observable. Everyday examples of measurements, such as the amount of gasoline pumped into a car, are presented, and the consequences of variability within those measurements are discussed. To gauge the success of the material, students' initial and subsequent understanding of variability and their attitude toward the usefulness of statistics were analyzed in a comparative study. Questions from the CAOS and ARTIST assessments that pertain to using variability to make

Adult Americans are encouraged to engage in at least 150 minutes of moderate to vigorous physical activity (MVPA) each week. National surveys that collect physical activity data to assess whether or not adults adhere to this guideline use self-report questionnaires that are prone to measurement error and nonresponse. Studies have examined the individual effects of each of these error sources on estimators of physical activity, but little is known about the consequences of not adjusting for both error sources. We conducted a simulation study to determine how estimators of adherence to the guideline for adults to engage in 150 minutes of MVPA each week respond to different magnitudes of measurement and nonresponse errors in self-reported physical activity survey data. Estimators that adjust for both measurement and nonresponse errors provide the least amount of bias regardless of the magnitudes of measurement error and nonresponse. In some scenarios, the naïve estimator, which does not adjust for either error source, results in less bias than estimators that adjust for only one error source. To avoid biased physical activity estimates using data collected from self-report questionnaires, researchers should adjust for both measurement error and nonresponse.