Literature Index

Displaying 2171 - 2180 of 3326

  • Statistical reasoning, thinking, and literacy: selected readings.

    Editors:
    Garfield, J., & Ben-Zvi, D.
    Year:
    1999

  • Statistical reasoning: Learning to reason from samples

    Editors:
    Ben-Zvi, D., Bakker, A., & Makar, K.
    Year:
    2015

  • Statistical reasoning: Valid intuitions put to use.

    Author(s):
    Sedlmeier, P.
    Editors:
    Lovett, M. C., & Shah, P.
    Year:
    2007

  • Statistical Reasoning: What is the Role of Interential Rule Training? Comment on Fong and Nisbett

    Author(s):
    Ploger, D., & Wilson, M.
    Year:
    1991
    Abstract:
    Fong and Nisbett (1991) argued that, within the domain of statistics, people possess abstract rules; that the use of these rules can be improved by training; and that these training effects are largely independent of the training domain. Although their results indicate that there is a statisically significant improvement in performance due to training, they also indicate that, even after training, most college students do not apply that training to example problems.

  • Statistical significance testing should be discontinued in mathematics education research

    Author(s):
    Menon, R.
    Year:
    1993
    Abstract:
    It is claimed here that the confidence mathematics education researchers have in statistical significance testing (SST) as an inference tool par excellence for experimental research is misplaced. Five common myths about SST are discussed, namely that SST: (a) is a controversy-free, recipe-like method to allow decision making; (b) answers the question whether there is a low probability that the research results were due to chance; (c) logic parallels the logic of mathematical proof by contradiction; (d) addresses the reliability /replicability question; and (e) is a necessary but not sufficient condition for the credibility of results. It is argued that SST's contribution to educational research in genera, and mathematics education research in particular, as not beneficial, and that SST should be discontinued as a tool for such research. Some alternatives to SST are suggested, and a call is made for mathematics education researchers to take the lead in using these alternatives.

  • Statistical significance testing: providing historical perspective for Menon's paper

    Author(s):
    Clements, M. A.
    Year:
    1993
    Abstract:
    I recall, in the mid-1970s, a research student of mine who, on carrying out an analysis of her data using statistical significance testing (SST), found that the p value, for what she regarded as her most important hypothesis, was .07, which was not significant at the .05 level. The student asked whether it would be legitimate for her to change the 2-tailed test she had use to a 1-tailed test, and on receiving a negative answer from me, went away disappointed. A couple of days later she returned saying that she had decided to remove some of the "outliers" from the data set, and that when these were removed she had got a p value of .04. In her thesis she honestly reported the sequence of events, but still claimed that she had obtained a "statistically significant" result. The external examiners for her thesis accepted this as legitimate tactic.

  • Statistical Thinking Activities: Some Simple Exercises with Powerful Lessons

    Author(s):
    Melton, K. I.
    Year:
    2004
    Abstract:
    Statistical thinking is required for good statistical analysis. Among other things, statistical thinking involves identifying sources of variation. Students in introductory statistics courses seldom recognize that one of the largest sources of variation may come in the collection and recording of the data. This paper presents some simple exercises that can be incorporated into any course (not just statistics) to help students understand some of the sources of variation in data collection. Primary attention is paid to operational definitions used in the data collection process.
    Location:
    http://www.amstat.org/publications/jse/v12n2/melton.html

  • Statistical thinking and learning

    Author(s):
    Greer, B.
    Year:
    2000
    Abstract:
    In inclusion of a double issue devoted to statistical thinking and learning to begin the second volume of "Mathematical Thinking and Learning" reflects major developments within statistics education during recent years. Statistics has entered or gained increased prominence in mainstream mathematics currcicula in many countries.

  • Statistical thinking and statistical practice: Themes gleaned from professional statisticians

    Author(s):
    Pfannkuch, M. & Wild, C. J.
    Year:
    2000
    Abstract:
    Advancing computer technology is allowing us to downplay instruction in mechanical procedures and shift emphasis towards teaching the "art" of statistics. This paper is based upon interviews with six professional statisticians about statistical thinking and statistical practice. It presents themes emerging from their professional experience, emphasizing dimensions that were surprising to them and were not part of their statistical training. Emerging themes included components of statistical thinking, pointers to good statistical practices and the subtleties of interacting with the thinking of others, particularly coworkers and clients. The main purpose of the research is to uncover basic elements of applied statistical practice and statistical thinking for the use of teachers of statistics.
    Location:
    http://projecteuclid.org/Dienst/UI/1.0/Summarize/euclid.ss/1009212754

  • Statistical Thinking For Effective Management

    Author(s):
    John, J. A. & Johnson, D. G.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Most managers do not instinctively think statistically, mainly because they are not convinced that statistical thinking adds any value to management and decision-making. Traditional business statistics courses tend to reinforce this view by concentrating on mathematical detail and computation. Without the ability to think statistically, and to understand and interpret data, managers have to resort to gut reactions, which are invariably misguided and unreliable. In this paper we advocate a problem centred approach to teaching statistical thinking based on realistic business examples. Students must be thoroughly involved in the learning process, and encouraged to discover for themselves the meaning, importance and relevance of statistical concepts. Time should be devoted to thinking about the key issues, and for significant interaction both between student and teacher and also, more importantly, between the students themselves.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/1/5b1_john.pdf

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The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education