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The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
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Updated: Jul 7, 2026

Improving Student Outcomes with an Adaptable Molecular Cloning Course-Based Undergraduate Research Experience
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Published on: November 15, 2024

Comparing undergraduate learning outcomes and experiences across code-based and non-code-based statistical software

Lisa Dierker1, Janet Rosenbaum2, Brandie Pugh3

  • 1Department of Psychology, Wesleyan University, Middletown, CT, USA. ldierker@wesleyan.edu.

BMC Medical Education
|July 5, 2026
PubMed
Summary

Students using code-based statistical software in introductory courses reported greater engagement and interest in data analysis. However, they also found the course more challenging and felt less prepared for advanced work.

Keywords:
Code-based statistical softwareData analysis skillsUndergraduate educationWorkforce preparation

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Published on: September 17, 2019

Area of Science:

  • Statistics education
  • Undergraduate learning outcomes
  • Computational statistics

Background:

  • Introductory statistics courses are crucial for developing data literacy.
  • The choice of statistical software (code-based vs. non-code-based) may influence student experiences and learning.
  • Understanding these differences can inform curriculum development and pedagogical approaches.

Purpose of the Study:

  • To compare learning outcomes and student experiences between code-based and non-code-based statistical software in undergraduate introductory statistics.
  • To identify specific impacts of using platforms like R, Python, or SPSS on student engagement, perceived learning, and future preparedness.

Main Methods:

  • A large-scale study involving 2,241 undergraduate students across 61 institutions using the Passion-Driven Statistics curriculum.
  • Comparison of outcomes between students using code-based (R, SAS, Stata, Python) and non-code-based (SPSS, Excel, JMP, StatCrunch) software.
  • Statistical analysis using mixed-effects cumulative logit and logistic regression models, controlling for covariates.

Main Results:

  • Students using code-based software reported working harder and finding the course more challenging.
  • Code-based learning was associated with higher perceived gains in data analysis, increased excitement, and greater research interest.
  • Students using non-code-based software felt more prepared for advanced coursework and thesis work.

Conclusions:

  • Learning statistical software with code enhances engagement and interest in data-driven tasks, despite a steeper learning curve.
  • Incorporating code-based tools in undergraduate statistics curricula can be valuable but requires adequate student support.
  • Findings suggest a trade-off between immediate perceived preparedness and long-term engagement with computational data analysis.