Module 4-1 – Why Randomization Matters and What Does it Accomplish (16-Minute Video)

• Remember gym class, when the teacher made everyone count off “one, two, one, two” to split people into fair groups? 

That simple trick is at the core of what scientists call randomization. In this module, we dive into how randomization creates fair comparisons in research, why it’s considered the gold standard for testing cause and effect, and how it cuts through misleading correlations. We’ll also clear up some of the most common myths about randomization.

• Presentation Slides

** The video's content and narration were generated with the assistance of artificial intelligence, with human guidance and oversight throughout the process. **

Seven myths of randomization in clinical trials (Source) 

• “Seven Myths of Randomization in Clinical Trials,” by Stephen Senn, tackles common misunderstandings about how randomization works in medical research. The paper explains why randomization isn’t a waste of efficiency, why it doesn’t prevent balancing groups, and why bigger trials aren’t automatically more valid. In clear terms, it shows how randomization protects studies from hidden biases and helps us separate real cause-and-effect from coincidence. It’s a reminder that while randomization may seem like just a coin flip, it’s actually one of science’s most powerful tools for trustworthy evidence.

• Full text available through institutional library access.

Stat Med. 2013; 32(9): 1439-1450. doi: 10.1002/sim.5713.

Simple randomization did not protect against bias in smaller trials (Source) 

• This study looked at whether simple randomization always protects against bias in clinical trials. The authors found that in smaller trials—those with fewer than about 1,000 participants—chance imbalances between groups can still create misleading results. Larger trials smooth out these random differences, but when smaller studies are necessary, researchers may need alternative methods like blocking or minimization to keep groups balanced. The takeaway: while randomization is powerful, sample size plays a critical role in whether results are truly reliable.

• Full text available through institutional library access.

J Clin Epidemiol. 2017; 84: 105-113. doi: 10.1016/j.jclinepi.2017.02.010.

Randomization and allocation concealment: a practical guide for researchers (Source) 

• This practical guide walks researchers through how to properly keep treatment assignments secret in clinical trials—an essential step known as allocation concealment. It focuses on a simple, cost-effective method called SNOSE—Sequentially Numbered, Opaque, Sealed Envelopes—and explains how to prepare them correctly for various trial designs: simple randomization, stratified randomization, permuted blocks, and multi-site studies.

J Crit Care. 2005; 20(2):187-191. doi: 10.1016/j.jcrc.2005.04.005.