Paired Multiple-Choice Questions Reveal Students’ Incomplete Statistical Thinking about Variation during Data Analysis

ABSTRACT Biologists consider variability during biological investigations. A robust quantitative understanding of variability is particularly important during data analysis, where statistics are used to quantify variation and draw conclusions about phenomena while accounting for variation. Many stud...

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Autores principales:	Jenna Hicks, Jessica Dewey, Michael Abebe, Yaniv Brandvain, Anita Schuchardt
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2021
Materias:	education assessments statistics undergraduate variation Special aspects of education LC8-6691 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6f46b58ffdad4692a6eff55db8683ae2
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spelling	oai:doaj.org-article:6f46b58ffdad4692a6eff55db8683ae22021-11-15T15:04:51ZPaired Multiple-Choice Questions Reveal Students’ Incomplete Statistical Thinking about Variation during Data Analysis10.1128/jmbe.00112-211935-78851935-7877https://doaj.org/article/6f46b58ffdad4692a6eff55db8683ae22021-09-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/jmbe.00112-21https://doaj.org/toc/1935-7877https://doaj.org/toc/1935-7885ABSTRACT Biologists consider variability during biological investigations. A robust quantitative understanding of variability is particularly important during data analysis, where statistics are used to quantify variation and draw conclusions about phenomena while accounting for variation. Many students struggle to correctly apply a quantitative understanding of variation to statistically analyze data. We present quantitative and qualitative analyses of introductory biology students’ responses on two pairs of multiple-choice questions querying two concepts related to the quantitative analysis of variation. More students correctly identify a mathematical expression of variation than correctly interpret it. Many students correctly interpret a nonsignificant p-value in the context of a very small sample size, but fewer students do so in the context of a large sample size. These results imply that many students have an incomplete quantitative understanding of variation. These findings suggest that instruction focusing on conceptual understanding, not procedural problem solving, may elevate students’ quantitative understanding of variation.Jenna HicksJessica DeweyMichael AbebeYaniv BrandvainAnita SchuchardtAmerican Society for MicrobiologyarticleeducationassessmentsstatisticsundergraduatevariationSpecial aspects of educationLC8-6691Biology (General)QH301-705.5ENJournal of Microbiology & Biology Education, Vol 22, Iss 2 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	education assessments statistics undergraduate variation Special aspects of education LC8-6691 Biology (General) QH301-705.5
spellingShingle	education assessments statistics undergraduate variation Special aspects of education LC8-6691 Biology (General) QH301-705.5 Jenna Hicks Jessica Dewey Michael Abebe Yaniv Brandvain Anita Schuchardt Paired Multiple-Choice Questions Reveal Students’ Incomplete Statistical Thinking about Variation during Data Analysis
description	ABSTRACT Biologists consider variability during biological investigations. A robust quantitative understanding of variability is particularly important during data analysis, where statistics are used to quantify variation and draw conclusions about phenomena while accounting for variation. Many students struggle to correctly apply a quantitative understanding of variation to statistically analyze data. We present quantitative and qualitative analyses of introductory biology students’ responses on two pairs of multiple-choice questions querying two concepts related to the quantitative analysis of variation. More students correctly identify a mathematical expression of variation than correctly interpret it. Many students correctly interpret a nonsignificant p-value in the context of a very small sample size, but fewer students do so in the context of a large sample size. These results imply that many students have an incomplete quantitative understanding of variation. These findings suggest that instruction focusing on conceptual understanding, not procedural problem solving, may elevate students’ quantitative understanding of variation.
format	article
author	Jenna Hicks Jessica Dewey Michael Abebe Yaniv Brandvain Anita Schuchardt
author_facet	Jenna Hicks Jessica Dewey Michael Abebe Yaniv Brandvain Anita Schuchardt
author_sort	Jenna Hicks
title	Paired Multiple-Choice Questions Reveal Students’ Incomplete Statistical Thinking about Variation during Data Analysis
title_short	Paired Multiple-Choice Questions Reveal Students’ Incomplete Statistical Thinking about Variation during Data Analysis
title_full	Paired Multiple-Choice Questions Reveal Students’ Incomplete Statistical Thinking about Variation during Data Analysis
title_fullStr	Paired Multiple-Choice Questions Reveal Students’ Incomplete Statistical Thinking about Variation during Data Analysis
title_full_unstemmed	Paired Multiple-Choice Questions Reveal Students’ Incomplete Statistical Thinking about Variation during Data Analysis
title_sort	paired multiple-choice questions reveal students’ incomplete statistical thinking about variation during data analysis
publisher	American Society for Microbiology
publishDate	2021
url	https://doaj.org/article/6f46b58ffdad4692a6eff55db8683ae2
work_keys_str_mv	AT jennahicks pairedmultiplechoicequestionsrevealstudentsincompletestatisticalthinkingaboutvariationduringdataanalysis AT jessicadewey pairedmultiplechoicequestionsrevealstudentsincompletestatisticalthinkingaboutvariationduringdataanalysis AT michaelabebe pairedmultiplechoicequestionsrevealstudentsincompletestatisticalthinkingaboutvariationduringdataanalysis AT yanivbrandvain pairedmultiplechoicequestionsrevealstudentsincompletestatisticalthinkingaboutvariationduringdataanalysis AT anitaschuchardt pairedmultiplechoicequestionsrevealstudentsincompletestatisticalthinkingaboutvariationduringdataanalysis
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Paired Multiple-Choice Questions Reveal Students’ Incomplete Statistical Thinking about Variation during Data Analysis

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