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Goals of Statistics Education
Statistics educators have cognitive and noncognitive goals for students. For example, former American Statistical Association President Katherine Wallman defined statistical literacy as including the cognitive abilities of understanding and critically evaluating statistical results as well as appreciating the contributions statistical thinking can make.

Cognitive Goals
In the text rising from the 2008 joint conference of the International Commission on Mathematical Instruction and the International Asssociation of Statistics Educators, editors Carmen Batanero, Gail Burrill, and Chris Reading (Universidad de Granada, Spain, Michigan State University, USA, and University of New England, Australia, respectively) note world-wide trends in curricula which reflect data-oriented goals. In particular, educators currently seek to have students: “design investigations; formulate research questions; collect data using observations, surveys, and experiments; describe and compare data sets; and propose and justify conclusions and predictions based on data.” The authors note the importance of developing statistical thinking and reasoning in addition to statistical knowledge.

Despite the fact that cognitive goals for statistics education increasingly focus on statistical literacy, statistical reasoning, and statistical thinking rather than on skills, computations and procedures alone, there is no agreement about what these terms mean or how to assess these outcomes. A first attempt to define and distinguish between these three terms appears in the ARTIST website (https://apps3.cehd.umn.edu/artist/publications.html ) which was created by Garfield, delMas and Chance and has since been included in several articles and book chapters.

Brief definitions of these terms are as follows: Statistical literacy is being able to read and use basic statistical language and graphical representations to understand statistical information in the media and in daily life. Statistical reasoning is being able to reason about and connect different statistical concepts and ideas, such as knowing how and why outliers affect statistical measures of center and variability. Statistical thinking is the type of thinking used by statisticians when they encounter a statistical problem. This involves thinking about the nature and quality of the data and, where the data came from, choosing appropriate analyses and models, and interpreting the results in the context of the problem and given the constraints of the data.
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Further cognitive goals of statistics education vary across students' educational level and the contexts in which they expect to encounter statistics.

Statisticians have proposed what they consider the most important statistical concepts for educated citizens. For example, Utts (2003) published seven areas of what every educated citizen should know, including understanding that “variability is normal” and that “coincidences… are not uncommon because there are so many possibilities.” Meanwhile, Gal (2002) suggests adults in industrialized societies are expected to exercise statistical literacy, “the ability to interpret and critically evaluate statistical information… in diverse contexts, and the ability to… communicate understandings and concerns regarding the… conclusions.”