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We’re pleased to announce the launch of a new learning unit on randomization! This unit was built in collaboration with Mary Harrington and Lisa Mangiamele of Smith College to help learners improve rigor as well as reliability of their results by using randomization in research design.
Like our other units, each lesson blends theoretical insights with hands-on activities. This unit ensures that learners understand how randomization can mitigate factors that interfere with results and gain the skills to apply it effectively. Real-world examples and interactive exercises also prompt reflection on research habits and help cultivate decision-making practices grounded in rigor.
What do we mean when we say randomization improves rigor? Randomization enhances the credibility of scientific findings. It reduces bias, controls for confounding variables, and helps ensure that observed effects are truly due to the intervention and not an alternative.
Randomization in experimental design is about systematically and thoughtfully imposing an order onto our treatment of variables to reduce the interference of confounding variables in our study. This is not just about checking a methodological box. A well-randomized study is harder to challenge, more likely to be reproducible, and better positioned to inform real-world decisions.
While randomization is often discussed in terms of assigning treatments to groups (random allocation), there are multiple points in an experiment where randomization should be used. This unit highlights several of these including timing, spatial arrangements, and team member assignments, but maintains a primary focus on treatment allocation.
Why explore this randomization unit before launching your next study? Randomization needs to be part of your experimental design from the very beginning. Waiting until data collection (or reporting) begins is too late.
This eight-lesson unit is a practical and accessible guide for scientists who want to improve their experimental design. It begins with the foundational “why” behind randomization and walks learners through strategies for avoiding common pitfalls and selecting the most appropriate randomization method for a given study.
Core lessons introduce simple, block, and stratified randomization then guide learners on when and how to implement each approach effectively based on sample size, covariates, and logistical constraints. The final two lessons broaden the scope to explore additional uses of randomization in the lab and examines how randomization is reported in the literature coupled with guidance on best practices for documenting randomization in alignment with established field standards.
While researchers already appreciate the importance of randomization, they still seek concrete guidance on how to incorporate it into daily practice. That’s where this unit can help.
This unit can be found in the 3-lesson, 45 minute Meeting version as well as in the 8-lesson, 3 hour Class version.
Check it out here!
If you’re an educator looking to teach this topic, consider using the Class version of our unit for about a week’s worth of lecture. If you’re a student looking for something to do new in your lab meetings, try the Meeting version! No matter who you are, if you try using one of our units, let us know how it goes by emailing us at c4r@seas.upenn.edu.
Reach out to c4r@seas.upenn.edu if you have any questions about incorporating this unit into your classroom or presenting it in your lab group! We’re here to help however we can.