Competency in statistics is an integral component of the scientific method and has contributed enormously to the amount and sophistication professional knowledge within the academic disciplines. Despite these contributions, the value of statistics varies widely in the general population. On one hand, Davenport and Harris (2007) describe the essential importance of statistics by referencing the father of statistics, C. Edward Deming (1900-1993): In God we trust; all others bring data. Yet, Smith (2010) references a skeptical view of statistics popularized by Mark Twain (1835-1910): There are three kinds of lies: lies, damned lies, and statistics.
Despite these widely divergent views, many students approach the study of statistics with much fear, trepidation and anxiety. Indeed, a random survey of students entering a graduate-level education program rated the course requirement in statistics as the least desirable of all courses required for their academic major (Dykeman, 2010), and approximately 75% to 80% of graduate students in the social sciences appear to experience uncomfortable levels of statistics anxiety which negatively affect learning (Onwuegbuzie, Slate, et al., 2000; Onwuegbuzie & Seaman, 1995).
Many students delay taking statistics until the very end of their required curriculum. Yet, the successful understanding of statistics contributes enormously to the development of critical thinking skills needed to evaluate the professional literature presented in the social science and professional education curriculum.
The pedagogy of statistics is an active concern of statistics educators, and this pedagogy includes a variety of topics of crucial concern to the development of student competency. A survey of 162 articles in three journals of statistics education from 2005 through 2009 indicated the following topics, with percentages of total topic presentations indicated in parentheses (van der Merwe & Wilkinson, 2010): teaching and learning (29%), statistical reasoning (25%), computer use (15%), course design (12%), non-cognitive factors (10%), and non-empirical studies (9%). As noted, studies in the pedagogy of statistics education are directed to the (1) content of statistical analysis, (2) use of computers and software for statistical analysis, (3) teaching and learning strategies, and (4) non-cognitive factors that can either help or hinder student acquisition of skills needed to perform statistical analysis.
Students come to their course work in statistics with varying degrees personality dispositions and academic experiences that can either help or hinder their ability to do well. The antecedents of statistics anxiety are described in Balogly's (2001) structural equation modeling of 246 university students: (1) dispositional factors, such as perceived task difficulty and degree of ego threat; (2) situational factors, such as the immediate factors surrounding the stimulus events; and (3) environmental factors, such as age, gender and relevant background experience. These antecedents influence the amount of trait anxiety brought to the study of statistics by each student as well as the state anxiety each student experiences when responding to stressors in their immediate situation. Dispositional and environmental factors interact with situational stressors to produce varying amounts of facilitative and debilitative anxiety (Alpert & Haber, 1960). Indeed, high-anxiety students in high-stress evaluative conditions demonstrate more emotionality and poorer performance than students in either high anxiety-low stress, low anxiety-high stress, or low anxiety-low stress conditions (Deffenbacher, 1978). In this regard, student responses to the stress induced by the study of statistics will vary widely, yet many students look upon the study of statistics as a high-stress condition and will avoid or delay their required statistics class during their academic studies. …