Given the size and complexity of horticulture data sets, researchers require statistical analysis software to organize, analyze, and illustrate results. As with other research tools, students learn to use statistical software through formal instruction (courses) or informal coaching. Universities offer a plethora of statistics courses covering several statistical software packages (Lazar et al., 2011; Mazouchova et al., 2021). Additionally, data analysis and interpretation modules are becoming popular in discipline-specific courses (Schwab-McCoy, 2019). Thus, when selecting which courses to enroll in, students are implicitly selecting which statistical software to learn.
Statistical analysis is a common expectation placed on graduates of plant science and horticulture programs (Richter et al., 2018). Courses are the primary source of training in data analysis for students (Davidson et al., 2019). Thus, course selection can influence student career readiness. Graduate students frequently select their courses with input from their advisors and/or other senior laboratory members, whereas undergraduate students frequently follow predetermined course requirements. When they have multiple courses to choose from or elective credits, undergraduate students have less information than graduate students because they are advised by university personnel who are not aware of horticulture research practices. Hence, there is a need for empirical data that can inform student and advisor choices. In this article, we provide the first empirical report on statistical software use in horticulture research articles with the goal of informing students and educators who might need to select courses or train in statistics.
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