Ten simple rules for getting started with statistics in graduate school

Zitomer, Rachel A.; Karr, Jessica; Kerstens, Mark; Perry, Lindsey; Ruth, Kayla; Adrean, Lindsay J.; Austin, Suzanne H.; Cornelius, Jamie M.; Dachenhaus, Jonathan; Dinkins, Jonathan B.; Harrington, Alan; Kim, Hankyu; Owens, Terrah; Revekant, Claire; Schroeder, Vanessa M.; Sink, Chelsea; Valente, Jonathon J.; Woodis, Ethan; Rivers, James W.

doi:10.1371/journal.pcbi.1010033

Cited by 1 publication

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the graduate level, in our experiences [ 5 ] and those of others [ 17 , 18 ], there exist learning gaps in statistical training in the sciences, and this is often the first time these students receive advanced training in statistics [ 19 ]. For instance, in numerous seminars, committee meetings, and oral exams that we and colleagues attended (Personal Communications, Gundula Bosch), we frequently observed students uncritically operating statistical software, proving incapable of providing sound rationales for the application of a particular method or just making sense of statistical data.…”

Section: Introductionmentioning

confidence: 99%

Teaching students to R3eason, not merely to solve problem sets: The role of philosophy and visual data communication in accessible data science education

Ciubotariu¹,

Bosch²

2023

PLoS Comput Biol

View full text Add to dashboard Cite

Much guidance on statistical training in STEM fields has been focused largely on the undergraduate cohort, with graduate education often being absent from the equation. Training in quantitative methods and reasoning is critical for graduate students in biomedical and science programs to foster reproducible and responsible research practices. We argue that graduate student education should more center around fundamental reasoning and integration skills rather than mainly on listing 1 statistical test method after the other without conveying the bigger context picture or critical argumentation skills that will enable student to improve research integrity through rigorous practice. Herein, we describe the approach we take in a quantitative reasoning course in the R3 program at the Johns Hopkins Bloomberg School of Public Health, with an error-focused lens, based on visualization and communication competencies. Specifically, we take this perspective stemming from the discussed causes of irreproducibility and apply it specifically to the many aspects of good statistical practice in science, ranging from experimental design to data collection and analysis, and conclusions drawn from the data. We also provide tips and guidelines for the implementation and adaptation of our course material to various graduate biomedical and STEM science programs.

show abstract