Evidence map›Paper›PMID 41770785›Full record

ArticlePloS one2026

Revealing undergraduate biology students' conception of variability and error bars within graphing.

Lauren Stoczynski, David Zis, Anna Woodruff, Susan Maruca, Eli Meir, Joel K Abraham, Stephanie M Gardner

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Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Lauren StoczynskiDepartment of Biological and Environmental Sciences, Le Moyne College, Syracuse, New York, United States of America.ORCID https://orcid.org/0000-0001-7041-2957
David ZisDepartment of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America.
Anna WoodruffDepartment of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America.ORCID https://orcid.org/0009-0004-7014-6272
Susan MarucaSimbiotic Software Inc. Research Division, Missoula, Montana, United States of America.
Eli MeirSimbiotic Software Inc. Research Division, Missoula, Montana, United States of America.
Joel K AbrahamDepartment of Biological Sciences, California State University at Fullerton, Fullerton, California, United States of America.
Stephanie M GardnerDepartment of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Creating and interpreting graphs are quantitative reasoning skills all science, technology, engineering, and math students need to develop. In biology, data are lacking in our understanding of how undergraduate students interpret variability across different graph types or what knowledge students have about the data error bars represent. This study analyzed 3506 student responses within a graphing assessment. We developed a code book for students' open-ended responses on the data error bars represent and used chi-square tests to look for significant differences in how students described error bars based on a graph they created and self-reported demographic data. We observed four major categories in how students described error bars: broad terms, error terms, purpose terms, and trend & analysis terms. When responses were linked with a graph the student made, those who created a bar graph with a bar for each data point showed lower understanding of error bars compared to students who themselves created a bar graph with aggregated means and error bars. We also observed differences in how students described error bars based on their major but did not observe differences based on whether students were in introductory or upper-level courses. Our results suggest that students are not receiving enough instruction on variability within graphing even though bar graphs with error bars are a common graph that students are asked to construct. More scaffolded repeated instruction is needed across biology curricula.

Indexed as

BiologyStudentsHumansUniversities

Identifiers

PMID41770785
PMCPMC12952588

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