Teaching for Statistical Literacy and Services of Statistics Agencies

By Gal, Iddo | The American Statistician, May 2003 | Go to article overview
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Teaching for Statistical Literacy and Services of Statistics Agencies


Gal, Iddo, The American Statistician


1. INTRODUCTION

Recent years have seen efforts to define and differentiate constructs related to the ability of people from all walks of life to act as informed consumers of statistical information. Educators, researchers, and professional organizations have attended to interrelated concepts such as statistical literacy (Wallman 1993), quantitative literacy (Landwehr, Swift, and Watkins 1987; Kirsch, Jungeblut, Jenkins, and Kolstad 1993; Steen 2001), mathematical literacy (OECD 2001), or numeracy (Gal et al. 1999). In addition, preliminary efforts examined the interdependencies between these constructs and reflected on the extent to which there is a developmental hierarchy; that is, whether some capacities have to be acquired before other capacities can emerge (Gal 2000; Garfield 2002; Watson 1997).

The commonalities and distinctions between the above and related concepts, or their exact nature and developmental stages, may continue to be a matter for dialogue and research (delMas 2002). Nonetheless, diverse sources seem clear about the need for people (including learners in both formal, nonformal, and workplace contexts) to develop the ability to comprehend, interpret, and critically evaluate messages with statistical elements or arguments conveyed by the media and other sources (European Commission 1996; NCTM 2000; Steen 2001). This ability will be termed here "statistical literacy' following an early use of this term by Wallman (1993).

What are the building blocks of statistical literacy, on which educators should focus their efforts? According to Gal (2002), understanding, interpreting, and reacting to real-world messages that contain statistical elements or findings require more than the possession of statistical knowledge per se. Such actions are founded on an interaction between several knowledge bases and supporting dispositions. Literacy skills must be activated, together with statistical knowledge, mathematical knowledge, and general world knowledge.

Critical evaluation of statistical messages also depends on people's knowledge of what questions to ask and on dispositions, such as their willingness to take a critical stance, their sense of comfort in engaging in what they perceive as mathematical tasks or using their quantitative knowledge, or their beliefs about the trustworthiness of statistical findings or the predictability of chance processes.

While the knowledge bases and beliefs and attitudes that underlie statistically literate behavior have been outlined in some detail, it is much less clear how to go about developing them. For example, Batanero (2002) discussed the need to develop micro-level models, and Rumsey (2002a) noted the lack of coherence between educational goals and the reality in textbooks and classes. This article aims to contribute to the efforts to promote statistical literacy by critically examining current approaches to teaching statistical literacy and identifying areas that need further attention by educators and researchers. In light of the problems identified, the article discusses the hitherto unexplored role of products of statistics agencies as a tool or resource that can support statistical literacy education. The article concludes with a discussion of the implications and needed collaboration between educators and statistics agencies.

2. ON METHODS FOR DEVELOPING STATISTICAL LITERACY

An analysis of recent literature on statistics education and adult numeracy points to two approaches towards developing learners' ability to act in a statistically literate way, through transfer of general statistical knowledge, or direct practice on critical questions and exemplar messages. This section discusses these approaches and problems with implementing them.

2.1 Direct and Indirect Methods

Curricular statements describing undergraduate statistics courses (Garfield, Hogg, Schau, and Whittinghill 2002) expect students to become sensitive to the value of statistical thinking and methods outside the classroom and turn into intelligent consumers of data.

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