Statistical adjustments to accommodate multiple comparisons are routinely covered in introductory statistical courses. The fundamental rationale for such adjustments, however, may not be readily understood. This article presents a simple illustration to help remedy this
In this paper, we highlight some qualitative facets of the discipline of statistics and argue that a qualitative approach, in particular a qualitative methodology known as phenomenography, allows us to research important aspects of statistics pedagogy. We summarize several components of our recent research into students' conceptions of statistics, their learning of statistics, our teaching of statistics, and their perceptions of their future professional work. We have obtained this information on the basis of analyses of several series of interviews with students studying statistics, both as statistics majors and as service students. In each of these cases, the broadest views relate in some way to personal connection, growth, and change. In other words, they contain a strong ontological component - focusing on being or becoming a statistician - above and beyond the standard epistemological component - focusing on the knowledge required to do statistics. We discuss the importance of personal change in becoming a statistician, or an informed professional user of statistics, and investigate the pedagogical conditions under which such change is likely to occur.
This paper seeks to add to a scholarly dialogue regarding the role and value of qualitative techniques in research on learning and using statistics. The paper briefly outlines some of the core assumptions of qualitative research methods, and presents four examples to illustrate selected qualitative methods that are used by educational researchers and service organizations. The discussion emphasizes the need to integrate quantitative and qualitative approaches in research on learners and users of statistics, and suggests that such integration may be needed to study emerging web-based communities of learners and users of statistics.
The introductory statistics course has traditionally targeted consumers of statistics with the intent of producing a citizenry capable of a critical analysis of basic published statistics. More recently, statistics educators have attempted to centre the intro course on real data, in part to motivate students and in part to create a more relevant course. The success of this approach is predicated on providing data that the students see as real and relevant. Modern students, however, have a different view of data than did students of 10 or even 5 years ago. Modern statistics courses must adjust to the fact that students' first exposure to data occurs outside the academy.
As Meng (2009) made clear, one of the statistics profession's responsibilities is to be "the first quantitative trainers of future generations of scientists, engineers, policy makers, etc." (not just statisticians). Evidence suggests we have not met this challenge. In fact, our traditional Stat101 courses and texts can poison the statistical well for the people who become our potential sponsors and collaborators. We need to do more than teach 'methods.' We need to show from the first day and throughout the Stat101 experience that our methods exist to help people learn interesting things about issues and topics they are passionate about. This message pertains to the rising generations of professionals and the citizenry at large and it applies to statisticians. Getting the message across may require radically redesigned 'service courses' and a new generation of uber-teachers as Meng (2009) advocated. In the meantime we should use existing materials in ways that show how subject-matter passion can motivate statistical analyses that reveal interesting and important subject-matter insights. As we develop new texts and other materials we need better quality control by authors, editors, and reviewers to assure that our teaching supports our "first quantitative trainer" responsibility.
In an important and timely article Meng (2009) has raised important questions regarding the future of the statistics profession. We elaborate on several of his points and offer some additional opportunities for the profession to consider. We argue that statistical methods and tools must be properly integrated into an overall approach to scientific inquiry in order to be properly understood and utilized. The discipline of statistical engineering, defined in this article, provides a mechanism to do this based on research and theory. Similarly, statistical thinking provides a clear framework to help students understand the "big picture" of statistics, and a relevant context for its application. Further, there is a natural, synergistic linkage between statistical thinking, statistical engineering, and statistical methods. We believe that teaching this linkage to students and utilizing it widely ourselves will enable the profession to move forward to a higher level of impact.
Xiao-Li Meng's recent article "Desired and Feared - What Do We Do Now and Over the Next 50 Years?" (2009) was of particular interest to me as a former undergraduate statistics major and as an Associate Professor who teaches 12 sections of introductory statistics annually at Montgomery College, a two-year college in Montgomery County, Maryland (an adjoining county to Washington, D.C.). I approach my comments from these perspectives as I believe that these groups very much need to be represented/addressed in the discussion of Meng's observations and proposals. My remarks are also influenced by a Washington Post article published during the 2009 Joint Statistical Meetings that referred to statisticians as "superheroes," described some of the challenges we face, and ultimately presented a favorable light (in my opinion) on our discipline.
The growing popularity of the statistical sciences has brought about an unprecedented student demand for undergraduate statistics courses, especially courses of an introductory nature. The question of "Who Is Teaching Introductory Statistics?" is at the core of whether over the next 50 years the discipline of statistics would be desired or feared. This commentary addresses compelling issues currently facing the status of statistics education in this nation.
An intense debate about Harvard University's General Education Curriculum demonstrates that statistics, as a discipline, is now both desired and feared. With this new status comes a set of enormous challenges. We no longer simply enjoy the privilege of playing in or cleaning up everyone's backyard. We are now being invited into everyone's study or living room, and trusted with the task of being their offspring's first quantitative nanny. Are we up to such a nerve-wracking task, given the insignificant size of our profession relative to the sheer number of our hosts and their progeny? Echoing Brown and Kass's "What Is Statistics?" (2009), this article further suggests ways to prepare our profession to meet the ever-increasing demand, in terms of both quantity and quality. Discussed are (1) the need to supplement our graduate curricula with a professional development curriculum (PDC); (2) the need to develop more subject oriented statistics (SOS) courses and happy courses at the undergraduate level; (3) the need to have the most qualified statisticians - in terms of both teaching and research credentials - to teach introductory statistical courses, especially those for other disciplines; (4) the need to deepen our foundation while expanding our horizon in both teaching and research; and (5) the need to greatly increase the general awareness and avoidance of unprincipled data analysis methods, through our practice and teaching, as a way to combat "incentive bias," a main culprit of false discoveries in science, misleading information in media, and misguided policies in society.
The introductory applied statistics course taken by many thousands of undergraduate students has undergone a transformation over the past 25 years. Changes in what we teach, how we teach, and how we assess have impacted introductory statistics courses at institutions worldwide. In this article we shift focus from what we teach and how we teach to when we teach. We propose changes to the sequence in which core statistical concepts are presented in an introductory applied statistics course. The proposed ordering of topics repeats the sequence of descriptive summaries - probability theory - statistical inference several times throughout the course in various contexts.