Teaching Statistical Inference Through a Conceptual Lens: A Spin on Existing Methods with Examples
AbstractUsing software to teach statistical inference in introductory courses opens the door for methods and practices that are more conceptually appealing to students. With an increasing number of fields requiring competency in statistics including data science, natural and social sciences, public...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-01-01
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| Series: | Journal of Statistics and Data Science Education |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/26939169.2023.2190011 |
| _version_ | 1797243493370822656 |
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| author | Mortaza Jamshidian Parsa Jamshidian |
| author_facet | Mortaza Jamshidian Parsa Jamshidian |
| author_sort | Mortaza Jamshidian |
| collection | DOAJ |
| description | AbstractUsing software to teach statistical inference in introductory courses opens the door for methods and practices that are more conceptually appealing to students. With an increasing number of fields requiring competency in statistics including data science, natural and social sciences, public health and more, it is crucial that we as instructors deliver the basic concepts of statistics effectively. In line with guidelines presented in the GAISE College Report, this article demonstrates intuitive approaches to teaching proportion and mean inference that take advantage of statistical software and emphasize conceptual understanding. The article recommends putting aside asymptotic-based methods for proportion inference and using the exact binomial method. Regarding mean inference, we propose a more contextualized and simplified process that uses the distribution of the sample mean directly and avoids standardized statistics such as z or t. In both the proportion and mean inference contexts, we discuss the benefits of the proposed approaches and provide detailed examples that demonstrate the methods using the Rguroo statistical software. |
| first_indexed | 2024-03-08T17:26:27Z |
| format | Article |
| id | doaj.art-38a0fefb2691420b9a952242525a9976 |
| institution | Directory Open Access Journal |
| issn | 2693-9169 |
| language | English |
| last_indexed | 2024-04-24T18:55:59Z |
| publishDate | 2024-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Statistics and Data Science Education |
| spelling | doaj.art-38a0fefb2691420b9a952242525a99762024-03-26T15:27:02ZengTaylor & Francis GroupJournal of Statistics and Data Science Education2693-91692024-01-01321547210.1080/26939169.2023.2190011Teaching Statistical Inference Through a Conceptual Lens: A Spin on Existing Methods with ExamplesMortaza Jamshidian0Parsa Jamshidian1Department of Mathematics, California State University, Fullerton, Fullerton, CADepartment of Biostatistics, University of California, Los Angeles, Los Angeles, CAAbstractUsing software to teach statistical inference in introductory courses opens the door for methods and practices that are more conceptually appealing to students. With an increasing number of fields requiring competency in statistics including data science, natural and social sciences, public health and more, it is crucial that we as instructors deliver the basic concepts of statistics effectively. In line with guidelines presented in the GAISE College Report, this article demonstrates intuitive approaches to teaching proportion and mean inference that take advantage of statistical software and emphasize conceptual understanding. The article recommends putting aside asymptotic-based methods for proportion inference and using the exact binomial method. Regarding mean inference, we propose a more contextualized and simplified process that uses the distribution of the sample mean directly and avoids standardized statistics such as z or t. In both the proportion and mean inference contexts, we discuss the benefits of the proposed approaches and provide detailed examples that demonstrate the methods using the Rguroo statistical software.https://www.tandfonline.com/doi/10.1080/26939169.2023.2190011Binomial exact testConstructing confidence intervalsIntroductory statisticsInverting a test of hypothesisRgurooStatistical software |
| spellingShingle | Mortaza Jamshidian Parsa Jamshidian Teaching Statistical Inference Through a Conceptual Lens: A Spin on Existing Methods with Examples Journal of Statistics and Data Science Education Binomial exact test Constructing confidence intervals Introductory statistics Inverting a test of hypothesis Rguroo Statistical software |
| title | Teaching Statistical Inference Through a Conceptual Lens: A Spin on Existing Methods with Examples |
| title_full | Teaching Statistical Inference Through a Conceptual Lens: A Spin on Existing Methods with Examples |
| title_fullStr | Teaching Statistical Inference Through a Conceptual Lens: A Spin on Existing Methods with Examples |
| title_full_unstemmed | Teaching Statistical Inference Through a Conceptual Lens: A Spin on Existing Methods with Examples |
| title_short | Teaching Statistical Inference Through a Conceptual Lens: A Spin on Existing Methods with Examples |
| title_sort | teaching statistical inference through a conceptual lens a spin on existing methods with examples |
| topic | Binomial exact test Constructing confidence intervals Introductory statistics Inverting a test of hypothesis Rguroo Statistical software |
| url | https://www.tandfonline.com/doi/10.1080/26939169.2023.2190011 |
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