In the pilot survey of undergraduate students’ views and values about data with my colleagues Drs. Kleintop, Carew, and Healy, I snuck in a question that I was particularly excited to see the results for. In this survey, students across five undergraduate courses spanning economics, policy studies, business, and history ranked their agreement on a scale from one to five with different statements such as “my personal experience and values do not influence the way I use or interpret data” and  “data speaks for itself.” The question I was excited to sneak in was: “Learning about the world always involves some degree of uncertainty.” 

I’ve repeatedly said that the statements we surveyed students about don’t have right and wrong answers, but reflect elements of our philosophies towards and beliefs about data. I both stand by that and feel very strongly that learning about the world does in fact always involve some degree of uncertainty. Maybe I’m wrong (uncertainty!) but I’ve yet to see clear evidence to the contrary, even given the sweeping “always” nature of this statement that ought to make it easy to disprove.  

Dr. Sarah Hamersma, an economist at Syracuse University, authored a piece for an online magazine a few years back that gets at this perspective. In her piece, “Uncertainty: The Beauty and Bedrock of Statistics” (2020), she marvels at the ways in which we deal with uncertainty in statistical analysis. Uncertainty, of course, is everywhere. In fact, as Dr. Hamersma writes, “the study of statistics is actually built on the notion that everything is uncertain.”  

Statistical inference and hypothesis testing in particular are all about using a limited set of information to try to say something about an unknown snippet of the world. These tools do not solve uncertainty; they put it at the forefront. Statistics is a way to be clear about the degree of uncertainty in given conditions.  

Teaching with Uncertainty  

When I teach my business statistics course, I sometimes use an in-class assignment centered on Dr. Hamersma’s piece. Students do a guided reading of the article and reflect, first alone and then in small groups, on the following questions: 

1. Does something about numbers seem “certain” to you? If so, why?
2. What evidence have you seen so far in this course that “statistics is actually built on the notion that everything is uncertain”? 
3. Based on what you’ve learned in this course, how is “the expression of the degree of uncertainty made explicit” in statistics? 
4. Does the author’s description of the use of confidence intervals fit with what you have learned about them so far? Explain. 
5. What do you think about the author’s claim that “We bristle under uncertainty.” Do you agree or disagree? Why or why not? 
6. What do you think is the role of statistics in saying something about questions with uncertain answers? 

For a class mostly focused on executing data analysis tasks in Excel, students are often a bit thrown by this exercise. Asking students to reflect on whether, as humans, we “bristle under uncertainty” is not part of standard statistics textbooks. But I like that the exercise asks them to sit with the higher-order thinking of statistics, rather than just plug-and-chug practice devoid of meaning. (And did you notice that I still snuck in a question about confidence intervals? The technical and philosophical aspects of statistics are actually quite complementary.) 

What Did the Students Think? 

In our survey, students overwhelmingly agreed with the statement (can I just call it a fact now, please?) that learning about the world always involves some degree of uncertainty. On a scale from 1 (strongly disagree) to 5 (strongly agree), the average rating was 4.5, the median 5, and over 90 percent of students rated the statement as a 4 or 5. This pattern was relatively consistent across the five courses, spanning multiple disciplines and professors, and in fact was the highest rated statement across all those that we asked about. Only one student out of 168 rated this statement below a 3, indicating active disagreement rather than neutrality or agreement.  

So maybe I’m not so radical in believing this, and maybe my students don’t even need me to make that connection. But I’ll still do it, and I’ll still ask them to think about the ways sitting with uncertainty is uncomfortable. Because only once we acknowledge that uncertainty exists can we move on to making the best decisions we can in spite of that fact. I want my students to go into the world not waiting for certainty to take action, but taking action when they’re “certain enough.” 

References 

Hamersma, Sarah. 2020. “Uncertainty: The Beauty and Bedrock of Statistics.” Comment. https://comment.org/uncertainty-the-beauty-and-bedrock-of-statistics/.  

Wigger, Cora. 2026. “What Does Data Say?” Center for Engaged Learning (blog). Elon University. August 11, 2026. https://www.centerforengagedlearning.org/what-does-data-say. 

About the Author 

Cora Wigger is an assistant professor of economics and a 2025–2027 CEL Scholar. Her research focuses on the intersections of education and housing policy, with an emphasis on racial inequality and desegregation. At Elon, she teaches statistics and data-driven courses and contributes to equity-centered initiatives like the “Quant4What? Collective” and the Data Nexus Faculty Advisory Committee.  

How to Cite This Post 

Wigger, Cora. 2026. “Pedagogy of Uncertainty.” Center for Engaged Learning (blog). Elon University. September 29, 2026. https://www.centerforengagedlearning.org/pedagogy-of-uncertainty/.