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Journal articles on the topic 'Learning statistics'

To see the other types of publications on this topic, follow the link: Learning statistics.

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Holt, Lori L., and Andrew J. Lotto. "What are the statistics in statistical learning?" Journal of the Acoustical Society of America 114, no. 4 (October 2003): 2444. http://dx.doi.org/10.1121/1.4779327.

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2

Hossain, Munier. "Learning statistics online." BMJ 335, no. 7621 (September 29, 2007): s119. http://dx.doi.org/10.1136/bmj.39273.644294.ce.

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3

Estes, Katharine Graf. "From Tracking Statistics to Learning words: Statistical Learning and Lexical Acquisition." Language and Linguistics Compass 3, no. 6 (September 4, 2009): 1379–89. http://dx.doi.org/10.1111/j.1749-818x.2009.00164.x.

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4

Nam Hai, Hoang. "ABOUT TEACHING AND LEARNING STATISTICS AT PRIMARY SCHOOLS." Journal of Science, Educational Science 60, no. 8A (2015): 231–35. http://dx.doi.org/10.18173/2354-1075.2015-0289.

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5

Jatnika, R., M. Haffas, and H. Agustiani. "Learning Statistics Using Universitas Padjadjaran Statistical Analysis Series." Journal of Physics: Conference Series 1179 (July 2019): 012046. http://dx.doi.org/10.1088/1742-6596/1179/1/012046.

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6

D’Orazio, Marcello. "Statistical learning in official statistics: The case of statistical matching." Statistical Journal of the IAOS 35, no. 3 (August 26, 2019): 435–41. http://dx.doi.org/10.3233/sji-190518.

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7

Kusumarasdyati. "Statistical reasoning or statistical method: Students’ preferences for learning Statistics." Journal of Physics: Conference Series 1417 (December 2019): 012041. http://dx.doi.org/10.1088/1742-6596/1417/1/012041.

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8

Zirmansyah, Zirmansyah. "Kualitas Skripsi Mahasiswa Universitas Al Azhar Indonesia: Pengaruh Hasil Belajar Metodologi Penelitian dan Statistik terhadap Kualitas Skripsi." JURNAL Al-AZHAR INDONESIA SERI HUMANIORA 1, no. 1 (April 4, 2011): 19. http://dx.doi.org/10.36722/sh.v1i1.20.

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The objective of the research is to study the relationship between learning outcome on statistic, learning outcome on research methodology and thesis quality. The research was carried out at the student Al Azhar University, with 53 samples of thesis which were selected randomly. The research concludes there is positive correlation between: (1) learning outcome on statistics and thesis quality; (2) knowledge on research methodology and thesis quality; (3) furthermore, there is a positive correlation between learning outcome on statistcs, learning outcome on research methodology, with thesis quality. Therefore thesis quality can be increased by improving learning outcome on statistic, and learning outcome on research metodhology.

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9

Balabdaoui, Fadoua, Lutz Dümbgen, Klaus-Robert Müller, and Richard Samworth. "Statistics meets Machine Learning." Oberwolfach Reports 17, no. 1 (February 9, 2021): 231–72. http://dx.doi.org/10.4171/owr/2020/4.

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10

Bzdok, Danilo, Naomi Altman, and Martin Krzywinski. "Statistics versus machine learning." Nature Methods 15, no. 4 (April 2018): 233–34. http://dx.doi.org/10.1038/nmeth.4642.

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11

Keeler, Carolyn M., and R. Kirk Steinhorst. "Cooperative Learning in Statistics." Teaching Statistics 16, no. 3 (September 1994): 81–84. http://dx.doi.org/10.1111/j.1467-9639.1994.tb00698.x.

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12

Bibby, Katherine, and Neville Davies. "STEPS for Learning Statistics." Teaching Statistics 17, no. 3 (September 1995): 107–10. http://dx.doi.org/10.1111/j.1467-9639.1995.tb00724.x.

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13

Llanos, Fernando, Yue Jiang, and Keith R. Kluender. "Exploiting second-order statistics improves statistical learning of vowels." Journal of the Acoustical Society of America 136, no. 4 (October 2014): 2082. http://dx.doi.org/10.1121/1.4899476.

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14

Curran-Everett, Douglas. "Explorations in statistics: statistical facets of reproducibility." Advances in Physiology Education 40, no. 2 (June 2016): 248–52. http://dx.doi.org/10.1152/advan.00042.2016.

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Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This eleventh installment of Explorations in Statistics explores statistical facets of reproducibility. If we obtain an experimental result that is scientifically meaningful and statistically unusual, we would like to know that our result reflects a general biological phenomenon that another researcher could reproduce if (s)he repeated our experiment. But more often than not, we may learn this researcher cannot replicate our result. The National Institutes of Health and the Federation of American Societies for Experimental Biology have created training modules and outlined strategies to help improve the reproducibility of research. These particular approaches are necessary, but they are not sufficient. The principles of hypothesis testing and estimation are inherent to the notion of reproducibility in science. If we want to improve the reproducibility of our research, then we need to rethink how we apply fundamental concepts of statistics to our science.

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15

Heinze-Deml, Christina, Marloes H. Maathuis, and Nicolai Meinshausen. "Causal Structure Learning." Annual Review of Statistics and Its Application 5, no. 1 (March 7, 2018): 371–91. http://dx.doi.org/10.1146/annurev-statistics-031017-100630.

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16

Bossaerts, Peter. "The Econometrics of Learning in Financial Markets." Econometric Theory 11, no. 1 (February 1995): 151–89. http://dx.doi.org/10.1017/s0266466600009075.

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The asymptotic behavior of the sample paths of two popular statistics that test market efficiency are investigated when markets learn to have rational expectations. Two cases are investigated, where, should markets start out at a rational expectations equilibrium, both statistics would asymptotically generate standard Brownian motions. In a first case, where agents are Bayesian and payoffs exogenous, the statistics have identical sample paths, but they are not standard Brownian motions. Whereas the finite-dimensional distributions are Gaussian, there may be a bias if agents' initial beliefs differ. A second case is considered, where payoffs are in part endogenous, yet agents consider them to be drawn from a stationary, exogenous distribution, which they attempt to learn in a frequentist way. In that case, one statistic behaves as if the economy were at a rational expectations equilibrium from the beginning on. The other statistic has sample paths with substantially non-Gaussian finite-dimensional distributions. Moreover, there is a negative bias. The behavior of the two statistics in the second case matches remarkably well the empirical results in an investigation of the prices of six foreign currency contracts over the period 1973–1990.

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17

Lelonkiewicz, Jarosław R., Michael T. Ullman, and Davide Crepaldi. "Knowledge of Statistics or Statistical Learning? Readers Prioritize the Statistics of their Native Language Over the Learning of Local Regularities." Journal of Cognition 5, no. 1 (February 21, 2022): 18. http://dx.doi.org/10.5334/joc.209.

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18

Salakhutdinov, Ruslan. "Learning Deep Generative Models." Annual Review of Statistics and Its Application 2, no. 1 (April 10, 2015): 361–85. http://dx.doi.org/10.1146/annurev-statistics-010814-020120.

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19

Xie, Jianwen, Ruiqi Gao, Erik Nijkamp, Song-Chun Zhu, and Ying Nian Wu. "Representation Learning: A Statistical Perspective." Annual Review of Statistics and Its Application 7, no. 1 (March 9, 2020): 303–35. http://dx.doi.org/10.1146/annurev-statistics-031219-041131.

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Learning representations of data is an important problem in statistics and machine learning. While the origin of learning representations can be traced back to factor analysis and multidimensional scaling in statistics, it has become a central theme in deep learning with important applications in computer vision and computational neuroscience. In this article, we review recent advances in learning representations from a statistical perspective. In particular, we review the following two themes: ( a) unsupervised learning of vector representations and ( b) learning of both vector and matrix representations.

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20

Clifton, Jesse, and Eric Laber. "Q-Learning: Theory and Applications." Annual Review of Statistics and Its Application 7, no. 1 (March 9, 2020): 279–301. http://dx.doi.org/10.1146/annurev-statistics-031219-041220.

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Q-learning, originally an incremental algorithm for estimating an optimal decision strategy in an infinite-horizon decision problem, now refers to a general class of reinforcement learning methods widely used in statistics and artificial intelligence. In the context of personalized medicine, finite-horizon Q-learning is the workhorse for estimating optimal treatment strategies, known as treatment regimes. Infinite-horizon Q-learning is also increasingly relevant in the growing field of mobile health. In computer science, Q-learning methods have achieved remarkable performance in domains such as game-playing and robotics. In this article, we ( a) review the history of Q-learning in computer science and statistics, ( b) formalize finite-horizon Q-learning within the potential outcomes framework and discuss the inferential difficulties for which it is infamous, and ( c) review variants of infinite-horizon Q-learning and the exploration-exploitation problem, which arises in decision problems with a long time horizon. We close by discussing issues arising with the use of Q-learning in practice, including arguments for combining Q-learning with direct-search methods; sample size considerations for sequential, multiple assignment randomized trials; and possibilities for combining Q-learning with model-based methods.

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21

Curran-Everett, Douglas. "Explorations in statistics: hypothesis tests and P values." Advances in Physiology Education 33, no. 2 (June 2009): 81–86. http://dx.doi.org/10.1152/advan.90218.2008.

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Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This second installment of Explorations in Statistics delves into test statistics and P values, two concepts fundamental to the test of a scientific null hypothesis. The essence of a test statistic is that it compares what we observe in the experiment to what we expect to see if the null hypothesis is true. The P value associated with the magnitude of that test statistic answers this question: if the null hypothesis is true, what proportion of possible values of the test statistic are at least as extreme as the one I got? Although statisticians continue to stress the limitations of hypothesis tests, there are two realities we must acknowledge: hypothesis tests are ingrained within science, and the simple test of a null hypothesis can be useful. As a result, it behooves us to explore the notions of hypothesis tests, test statistics, and P values.

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22

Smith, C. D. "Learning LOGO: effects on learning BASIC and statistics." Journal of Computer Assisted Learning 2, no. 2 (July 1986): 102–9. http://dx.doi.org/10.1111/j.1365-2729.1986.tb00072.x.

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23

Onodera, Takayoshi, Hironori Ootou, and Kana Suzuki. "Learning advanced statistics with jamovi." Proceedings of the Annual Convention of the Japanese Psychological Association 84 (September 8, 2020): TWS—004—TWS—004. http://dx.doi.org/10.4992/pacjpa.84.0_tws-004.

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24

PORCIÚNCULA MOREIRA DA SILVA, MAUREN, and SUZI SAMÁ PINTO. "TEACHING STATISTICS THROUGH LEARNING PROJECTS." STATISTICS EDUCATION RESEARCH JOURNAL 13, no. 2 (November 28, 2014): 177–86. http://dx.doi.org/10.52041/serj.v13i2.289.

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This paper aims to reflect on the teaching of statistics through student research, in the form of projects carried out by students on self-selected topics. The paper reports on a study carried out with two undergraduate classes using a methodology of teaching that we call ‘learning projects’. Monitoring the development of the various stages of the learning projects allowed continuous adjustment of the process and provided an insight into the benefits and limitations of this approach. Important aspects included the complexity of the group relationships, the importance of choosing the topic of the research, data collection and time management. Students carried out an evaluation of the process, and the resulting information was analysed using quantitative and qualitative approaches. First published November 2014 at Statistics Education Research Journal Archives

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25

Lipovetsky, Stan, and Jong-Min Kim. "Machine Learning in Applied Statistics." Model Assisted Statistics and Applications 12, no. 3 (August 31, 2017): 193–94. http://dx.doi.org/10.3233/mas-170404.

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26

Dietz, E. Jacquelin, and Thomas R. Knapp. "Learning Statistics Through Playing Cards." American Statistician 51, no. 2 (May 1997): 207. http://dx.doi.org/10.2307/2685422.

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27

Cavallo, Alberto, Guillermo Cruces, and Ricardo Perez-Truglia. "Learning from Potentially Biased Statistics." Brookings Papers on Economic Activity 2016, no. 1 (2016): 59–108. http://dx.doi.org/10.1353/eca.2016.0013.

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28

Beyea, Suzanne C. "Learning from sentinel event statistics." AORN Journal 80, no. 2 (August 2004): 315–18. http://dx.doi.org/10.1016/s0001-2092(06)60570-x.

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29

Pitas, I., C. Kotropoulos, N. Nikolaidis, R. Yang, and M. Gabbouj. "Order statistics learning vector quantizer." IEEE Transactions on Image Processing 5, no. 6 (June 1996): 1048–53. http://dx.doi.org/10.1109/83.503919.

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30

Stadler, Rolf, Rafael Pasquini, and Viktoria Fodor. "Learning from Network Device Statistics." Journal of Network and Systems Management 25, no. 4 (September 26, 2017): 672–98. http://dx.doi.org/10.1007/s10922-017-9426-z.

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31

Drton, Mathias, and Marloes H. Maathuis. "Structure Learning in Graphical Modeling." Annual Review of Statistics and Its Application 4, no. 1 (March 7, 2017): 365–93. http://dx.doi.org/10.1146/annurev-statistics-060116-053803.

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32

Chaamwe, Nchimunya, and Langstone Shumba. "ICT Integrated Learning: Using Spreadsheets as Tools for e-Learning, A Case of Statistics in Microsoft Excel." International Journal of Information and Education Technology 6, no. 6 (2016): 435–40. http://dx.doi.org/10.7763/ijiet.2016.v6.728.

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33

PETOCZ, PETER, and ANNA REID. "RELATIONSHIPS BETWEEN STUDENTS’ EXPERIENCE OF LEARNING STATISTICS AND TEACHING STATISTICS." STATISTICS EDUCATION RESEARCH JOURNAL 2, no. 1 (May 29, 2003): 39–53. http://dx.doi.org/10.52041/serj.v2i1.559.

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Students in the same statistics course learn different things, and view the role of the lecturer in different ways. We report on empirical research on students’ conceptions of learning statistics, their expectations of teaching, and the relationship between them. The research is based on interviews, analysed using a qualitative methodology, with statistics students studying for a mathematics degree. Students expressed a range of conceptions of learning in statistics and a range of conceptions of their lecturers’ teaching. These conceptions of learning and teaching were related, but not as closely or as exclusively as previous researchers have indicated. Looking at what students expect of teachers and their views of their own learning provides an opportunity for teachers to develop teaching practices that challenge students to move towards more integrated conceptions of statistics learning. First published May 2003 at Statistics Education Research Journal: Archives

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34

Liu, Qinghua, Marta Crispino, Ida Scheel, Valeria Vitelli, and Arnoldo Frigessi. "Model-Based Learning from Preference Data." Annual Review of Statistics and Its Application 6, no. 1 (March 7, 2019): 329–54. http://dx.doi.org/10.1146/annurev-statistics-031017-100213.

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Preference data occur when assessors express comparative opinions about a set of items, by rating, ranking, pair comparing, liking, or clicking. The purpose of preference learning is to ( a) infer on the shared consensus preference of a group of users, sometimes called rank aggregation, or ( b) estimate for each user her individual ranking of the items, when the user indicates only incomplete preferences; the latter is an important part of recommender systems. We provide an overview of probabilistic approaches to preference learning, including the Mallows, Plackett–Luce, and Bradley–Terry models and collaborative filtering, and some of their variations. We illustrate, compare, and discuss the use of these methods by means of an experiment in which assessors rank potatoes, and with a simulation. The purpose of this article is not to recommend the use of one best method but to present a palette of different possibilities for different questions and different types of data.

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35

Curran-Everett, Douglas. "Explorations in statistics: the bootstrap." Advances in Physiology Education 33, no. 4 (December 2009): 286–92. http://dx.doi.org/10.1152/advan.00062.2009.

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Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This fourth installment of Explorations in Statistics explores the bootstrap. The bootstrap gives us an empirical approach to estimate the theoretical variability among possible values of a sample statistic such as the sample mean. The appeal of the bootstrap is that we can use it to make an inference about some experimental result when the statistical theory is uncertain or even unknown. We can also use the bootstrap to assess how well the statistical theory holds: that is, whether an inference we make from a hypothesis test or confidence interval is justified.

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36

Curran-Everett, Douglas. "Explorations in statistics: permutation methods." Advances in Physiology Education 36, no. 3 (September 2012): 181–87. http://dx.doi.org/10.1152/advan.00072.2012.

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Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This eighth installment of Explorations in Statistics explores permutation methods, empiric procedures we can use to assess an experimental result–to test a null hypothesis–when we are reluctant to trust statistical theory alone. Permutation methods operate on the observations–the data–we get from an experiment. A permutation procedure answers this question: out of all the possible ways we can rearrange the observations we got, in what proportion of those arrangements is the sample statistic we care about at least as extreme as the one we got? The answer to that question is the P value.

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37

Puspitasari, Tari Okta, Nirmala Sari, Yolanda Eka Putri, and Nurdatul Jannah. "Attitude; Physics Learning Concentration." COMPTON: Jurnal Ilmiah Pendidikan Fisika 6, no. 2 (December 26, 2019): 13. http://dx.doi.org/10.30738/cjipf.v6i2.5850.

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The purpose of this study was to determine the relationship of attitudes to the concentration of student learning in physics at SMAN 10 Batang Hari. The sample used in this study was 121 with the data analysis technique used was purposive sampling. The instrument used was a questionnaire. The statistics used are descriptive statistics and inferential statistics. Where descriptive statistics display the mean, median, and mode. While inferential statistics use a correlation test. The results of this study are the relationship between attitudes to the concentration of learning in physics at SMAN 10 Batang Hari which is indicated by the sig size is 0.05.

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38

Hobbs, David, Mark C. K. Yang, and David H. Robinson. "Understanding and Learning Statistics by Computer." Mathematical Gazette 71, no. 457 (October 1987): 251. http://dx.doi.org/10.2307/3616795.

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39

Turmon, Michael J., Gholamreza Nakhaeizadeh, and Charles C. Taylor. "Machine Learning and Statistics: The Interface." Journal of the American Statistical Association 93, no. 442 (June 1998): 833. http://dx.doi.org/10.2307/2670132.

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40

Yousef, Darwish Abdulrahman. "Learning styles preferences of statistics students." Quality Assurance in Education 24, no. 2 (April 4, 2016): 227–43. http://dx.doi.org/10.1108/qae-01-2014-0004.

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Purpose – Although there are many studies addressing the learning styles of business students as well as students of other disciplines, there are few studies which address the learning style preferences of statistics students. The purpose of this study is to explore the learning style preferences of statistics students at a United Arab Emirates University (UAEU). Furthermore, it investigates whether there are statistically significant differences along the four dimensions of learning styles due to students’ demographic and academic characteristics. Design/methodology/approach – Questionnaires were distributed to the whole population which included 79 undergraduate statistics students at the UAEU, of which 69 returned the questionnaire. Descriptive statistics such as frequencies and percentages were used to present the main characteristics of respondents and the results of the study. Additionally, a chi-square test was used to find out if there were significant differences along the four dimensions of learning style preferences due to students’ demographic and academic characteristics. Findings – The results indicate that UAEU undergraduate statistics students have balanced preferences along the four dimensions of learning styles. Results also suggest that there are no statistically significant differences along the four dimensions of learning styles due to students’ demographic and academic characteristics, except in the active-reflective and sensing-intuitive dimensions with respect to high school type (private vs public). Research limitations/implications – There are a number of limitations associated with this study. First, the findings of the study are based on data from only one university. Second, the sample was small and limited to undergraduate statistics students and, therefore, it excluded graduate students who might have had different experiences. Third, the results are based on a self-reported questionnaire and this, in turn, might have affected the reliability of the results On the other hand, it has a number of implications for educators and students. Educators will benefit from the results of this study in the sense that they will adopt teaching styles and strategies that match learning styles of the majority of their students. Students themselves will benefit from knowing their own learning style. Originality/value – The present study is the first attempt to explore learning styles preference of undergraduate students not only in the UAE setting but also in the developing country setting.

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41

Delucchi, Michael. "Measuring Student Learning in Social Statistics." Teaching Sociology 42, no. 3 (March 19, 2014): 231–39. http://dx.doi.org/10.1177/0092055x14527909.

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42

Hunt, Neville, and Sidney Tyrrell. "Learning Statistics on the Web - DISCUSS." Teaching Statistics 22, no. 3 (October 2000): 85–90. http://dx.doi.org/10.1111/1467-9639.00032.

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43

Alexander, Melvin T. "Learning Statistics With the SAS System." Technometrics 33, no. 1 (February 1991): 110–12. http://dx.doi.org/10.1080/00401706.1991.10484779.

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44

Kader, Gary, and Mike Perry. "Power On!: Learning Statistics with Technology." Mathematics Teaching in the Middle School 1, no. 2 (September 1994): 130–36. http://dx.doi.org/10.5951/mtms.1.2.0130.

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In its Curriculum and Evaluation Standards for School Mathematics (1989), the National Council of Teachers of Mathematics recommends that the K-12 mathematics curriculum be broadened and designates statistics as an area deserving increased attention. The standards document promotes the concept that statistics be learned through the study of real problems with real data collected by the students. Rather than focus on developing formulas from which answers are simply computed, teachers should present statistics in a coherent fashion and develop the topic as a whole problem-solving process.

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45

Qin, Huihui, and Xin Guo. "Semi-supervised learning with summary statistics." Analysis and Applications 17, no. 05 (September 2019): 837–51. http://dx.doi.org/10.1142/s0219530519400037.

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Nowadays, the extensive collection and analyzing of data is stimulating widespread privacy concerns, and therefore is increasing tensions between the potential sources of data and researchers. A privacy-friendly learning framework can help to ease the tensions, and to free up more data for research. We propose a new algorithm, LESS (Learning with Empirical feature-based Summary statistics from Semi-supervised data), which uses only summary statistics instead of raw data for regression learning. The selection of empirical features serves as a trade-off between prediction precision and the protection of privacy. We show that LESS achieves the minimax optimal rate of convergence in terms of the size of the labeled sample. LESS extends naturally to the applications where data are separately held by different sources. Compared with the existing literature on distributed learning, LESS removes the restriction of minimum sample size on single data sources.

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46

Lai, Guolin, and Long Li. "Students’ Perceptions of Technology-Enhanced Pedagogy in Their Statistics Learning." Frontiers in Education Technology 1, no. 1 (May 25, 2018): 70. http://dx.doi.org/10.22158/fet.v1n1p70.

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<p><em>Statistical literacy, reasoning, and thinking are highly valued in various industries. However, many college students struggle in their required statistic course(s). The use of technology has the potential to bring about positive changes in content, pedagogy, and course format of statistics instruction. This study explores undergraduate business students’ perceptions of the instructor’s technology integration efforts in their statistics learning. The research results reveal that students mostly regarded their learning experience as positive, engaging, informative, and effective. They attributed their learning gain to the instructor’s innovative teaching style, the availability of various learning resources on Moodle, and how the resources were presented.</em><em></em></p>

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47

SW and E. Charniak. "Statistical Language Learning." Journal of the American Statistical Association 89, no. 428 (December 1994): 1570. http://dx.doi.org/10.2307/2291039.

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48

Rohana, Rohana, and Yunika Lestaria Ningsih. "STUDENTS’ STATISTICAL REASONING IN STATISTICS METHOD COURSE." Jurnal Pendidikan Matematika 14, no. 1 (December 31, 2019): 81–90. http://dx.doi.org/10.22342/jpm.14.1.6732.81-90.

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The role of statistics is wide and crucial in daily life, making statistics important. Many students have difficulty understanding statistics. This study aims to determine students' statistical reasoning about inference statistics, which is limited to the subject matter of the testing hypotheses about two-sample hypotheses testing. This study used descriptive research method. The subjects were 25 students of third-year Mathematics Education Departement at Universitas PGRI Palembang in the academic year 2018/2019. Data were collected through tests and interviews. Data were analyzed through descriptive quantitative. The results of data analysis showed that 32% of students had level 1 statistical reasoning (the lowest level), 20% were at level 2, 28% at level 3, 12% at level 4 and 8% at level 5 (highest level). Based on the result, it can conclude that students' statistical reasoning ability in learning statistical method is not satisfactory, students are still very lacking in reasoning.

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49

Zhao, J., L. Goldfarb, and N. B. Turk-Browne. "When numbers and statistics collide: Competition between numerosity perception and statistical learning." Journal of Vision 13, no. 9 (July 25, 2013): 1087. http://dx.doi.org/10.1167/13.9.1087.

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50

MCGINN, MICHELLE K. "LEARNING TO USE STATISTICS IN RESEARCH: A CASE STUDY OF LEARNING IN A UNIVERSITY-BASED STATISTICAL CONSULTING CENTRE." STATISTICS EDUCATION RESEARCH JOURNAL 9, no. 2 (November 29, 2010): 35–49. http://dx.doi.org/10.52041/serj.v9i2.374.

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This paper presents a qualitative case study of statistical practice in a university-based statistical consulting centre. Naturally occurring conversations and activities in the consulting sessions provided opportunities to observe questions, problems, and decisions related to selecting, using, and reporting statistics and statistical techniques in research. The consulting sessions provided simultaneous opportunities for consultants and clients to learn about using statistics in research. Consistent with contemporary theories that emphasize social dimensions of learning, major themes relate to (a) types of clients and consulting interactions, (b) disciplinary and statistical expertise, and (c) the role of material objects and representations. Evidence shows that consultants and clients learned during the consulting sessions and that the statistical consulting centre contributed positively to teaching and research at the university. First published November 2010 at Statistics Education Research Journal: Archives

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