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Statistical Thinking in Empirical Enquiry

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  • C. J. Wild
  • M. Pfannkuch

Abstract

This paper discusses the thought processes involved in statistical problem solving in the broad sense from problem formulation to conclusions. It draws on the literature and in‐depth interviews with statistics students and practising statisticians aimed at uncovering their statistical reasoning processes. From these interviews, a four‐dimensional framework has been identified for statistical thinking in empirical enquiry. It includes an investigative cycle, an interrogative cycle, types of thinking and dispositions. We have begun to characterise these processes through models that can be used as a basis for thinking tools or frameworks for the enhancement of problem‐solving. Tools of this form would complement the mathematical models used in analysis and address areas of the process of statistical investigation that the mathematical models do not, particularly areas requiring the synthesis of problem‐contextual and statistical understanding. The central element of published definitions of statistical thinking is “variation”. We further discuss the role of variation in the statistical conception of real‐world problems, including the search for causes. Le présent article concerne les processus mentaux impliqués dans la pensée statistique prise dans un sens large, depuis la formulation de problémes jusqu'á leur solution. II tire ses sources de la littérature sur le sujet ainsi que d'entrevues auprès d'étudiants et de praticiens en statistique, concues pour indentifier leurs processus de raisonnement statistique. De ces entrevues, nous avons identifié un cadre conceptuel quadridimensionel applicable à la pensée statistique dans le domaine de la recherche empirique. Ce cadre est composé d'un cycle d'investigation, d'un cycle d'interrogation, de types de pensée et de dispositions. NOus avons amocé la caractérisation de ces processus par des modèles pouvant servir de base àla création d'outils ou cadres intellectuels aidant la résolution de poblémes. Des outils de ce types pourraient complémenter les modèles mathématiques déjà utilisés en analyse en plus de couvrir certains aspects de la recherche statistique que les modèles mathématiques ne peuvent pas satisfaire, particulièrement les aspects associés à la synthèse des types contextuel et statistique de compréhension. l'élément central apparaissant dans les définitions de la pensée statistique ayant fait l'object de publication est celui de la “variation”. Nous discutons aussi le role de la variation dans l'approche statistique de problèmes pratiques, incluant la recherche de causes.

Suggested Citation

  • C. J. Wild & M. Pfannkuch, 1999. "Statistical Thinking in Empirical Enquiry," International Statistical Review, International Statistical Institute, vol. 67(3), pages 223-248, December.
  • Handle: RePEc:bla:istatr:v:67:y:1999:i:3:p:223-248
    DOI: 10.1111/j.1751-5823.1999.tb00442.x
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    Cited by:

    1. Seokmin Kang & Sungyeun Kim, 2022. "Lessons Learned from Topic Modeling Analysis of COVID-19 News to Enrich Statistics Education in Korea," Sustainability, MDPI, vol. 14(6), pages 1-16, March.
    2. Andreea Cujba & Manoli Pifarré, 2024. "Enhancing students’ attitudes towards statistics through innovative technology-enhanced, collaborative, and data-driven project-based learning," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    3. Laura Muñiz-Rodríguez & Luis J. Rodríguez-Muñiz & Ángel Alsina, 2020. "Deficits in the Statistical and Probabilistic Literacy of Citizens: Effects in a World in Crisis," Mathematics, MDPI, vol. 8(11), pages 1-20, October.
    4. Roger W. Hoerl & Ronald D. Snee, 2017. "Statistical Engineering: An Idea Whose Time Has Come?," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 209-219, July.
    5. Joel B. Greenhouse & Howard J. Seltman, 2018. "On Teaching Statistical Practice: From Novice to Expert," The American Statistician, Taylor & Francis Journals, vol. 72(2), pages 147-154, April.
    6. Heejoo Suh & Sohyung Kim & Seonyoung Hwang & Sunyoung Han, 2020. "Enhancing Preservice Teachers’ Key Competencies for Promoting Sustainability in a University Statistics Course," Sustainability, MDPI, vol. 12(21), pages 1-21, October.
    7. Maria Penna & Mirian Agus & Maribel Peró-Cebollero & Joan Guàrdia-Olmos & Eliano Pessa, 2014. "The use of imagery in statistical reasoning by university undergraduate students: a preliminary study," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(1), pages 173-187, January.
    8. Anand Desai, 2008. "Quantitative methods, economics, and or models," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 27(3), pages 640-669.
    9. Săvoiu, Gheorghe, 2008. "The scientifiv way of thinking in statistics, statistical physics and quantum mechanics," MPRA Paper 13558, University Library of Munich, Germany.
    10. Katja Prevodnik & Vasja Vehovar, 2014. "Presenting dynamics of social phenomena: should we use absolute, relative or time differences?," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(2), pages 799-816, March.
    11. Claudia Vásquez & Israel García-Alonso & María José Seckel & Ángel Alsina, 2021. "Education for Sustainable Development in Primary Education Textbooks—An Educational Approach from Statistical and Probabilistic Literacy," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
    12. Joan Franco Seguí & Ángel Alsina & Claudia Vásquez, 2024. "Teaching Statistics for Sustainability across Contexts: Exploring the Knowledge and Beliefs of Teachers," Sustainability, MDPI, vol. 16(2), pages 1-22, January.
    13. Ricardo Ocaña-Riola, 2016. "The Use of Statistics in Health Sciences: Situation Analysis and Perspective," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(2), pages 204-219, October.
    14. Gheorghe SAVOIU & Constantin MANEA, 2013. "Dimitrie Cantemir – The First Shaper of Romanian Statistical Thinking," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 61(4), pages 12-21, December.
    15. Idris Djouahra, 2022. "Conceptual understanding of linear regression among economics students at the university center of Tipaza, Algeria," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 8(2), pages 66-83, December.
    16. Theresa Büchter & Andreas Eichler & Nicole Steib & Karin Binder & Katharina Böcherer-Linder & Stefan Krauss & Markus Vogel, 2022. "How to Train Novices in Bayesian Reasoning," Mathematics, MDPI, vol. 10(9), pages 1-31, May.
    17. Jenna Hicks & Jessica Dewey & Yaniv Brandvain & Anita Schuchardt, 2020. "Development of the Biological Variation In Experimental Design And Analysis (BioVEDA) assessment," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-21, July.
    18. Patricia Belén Carrera & Luis R. Pino-Fan & Hugo Alvarado & Jesús Guadalupe Lugo-Armenta, 2021. "Practices of the Random Variable Proposed in the Chilean Mathematics Curriculum of Secondary Education," Mathematics, MDPI, vol. 9(19), pages 1-26, October.
    19. Jesús Guadalupe Lugo-Armenta & Luis Roberto Pino-Fan, 2021. "Inferential Reasoning of Secondary School Mathematics Teachers on the Chi-Square Statistic," Mathematics, MDPI, vol. 9(19), pages 1-20, September.
    20. Claudia Vásquez & Ángel Alsina, 2021. "Analysing Probability Teaching Practices in Primary Education: What Tasks Do Teachers Implement?," Mathematics, MDPI, vol. 9(19), pages 1-21, October.
    21. Gheorghe SAVOIU, 2012. "A Philosophical Introduction to the History of Statistics’ Conceptualization," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 60(2), pages 111-119, May.

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