Making sense of uncertainty in an increasingly complex world

By Siqhamo Jama

 

In a world awash with data, algorithms, and unpredictable outcomes, how we deal with uncertainty has become one of the defining questions of our time. Every weather forecast, medical test, or policy decision rests on probabilities – but what do those probabilities really mean?

That was the challenge posed by Professor Lizanne Raubenheimer from the Department of Statistics at Rhodes University, in her inaugural lecture, “To Bayes or Not to Bayes”. And far from being a jargon-heavy presentation of abstract equations and bar graphs, her talk centred on a very human fear – uncertainty: how we make sense of it, why it matters, how it shapes the choices we make, and how our interpretation of data shapes the decisions that affect everyday life.

A numerical future on the cards

Tracing her journey from a schoolgirl obsessed with the Maths 24 card game to a leading scholar in Bayesian statistics, Prof Raubenheimer shared a story marked by both curiosity and determination. “In class at school,” she recalled with a smile, “I wasn’t talking to friends – I was doing maths problems.”

Her love of numbers led her from actuarial science at the University of the Free State into the field of mathematical statistics – a place, she said, where logic meets creativity, and uncertainty becomes something to understand rather than fear.

The lecture’s title, a playful nod to Shakespeare’s Hamlet, captured a debate that has divided statisticians for decades: the choice between the Bayesian and frequentist schools of thought in dealing with uncertainty.

In simple terms, the frequentist sees probability as the long-run relative frequency of an event– a property that reveals itself over countless repetitions. The Bayesian, by contrast, treats probability as a matter of belief that evolves as new information becomes available.

“Think of it in the context of checking the weather,” she explained. “The frequentist says, ‘There’s a 40% probability of rain based on years of data.’ The Bayesian says, ‘Well, I already know it’s been cloudy all week, so I’ll adjust that belief.’”

When mathematics meets the real world

From spam filters and medical diagnoses to product recommendations and engineering systems, reliability theory quietly underpins much of modern life.

Prof Raubenheimer illustrated this through something as ordinary as a paper clip. “If you bend a paper clip back and forth,” she said, “you’re testing how long it takes before it breaks. Reliability theory does that on a larger scale with engines, aircraft components, or electronic parts.”

Her work explores how Bayesian methods can improve the mathematics of uncertainty or reliability theory, helping researchers and decision-makers make better choices even when data is limited. By combining existing knowledge with new evidence, Bayesian approaches allow sharper predictions from smaller datasets.

 “It’s not just about the numbers,” she said. “It’s about making informed choices in uncertain conditions.”

Learning from what we already know

By the end of the lecture, one truth was clear: statistics extends far beyond equations. It is a discipline devoted to understanding and navigating uncertainty – an ability increasingly vital in an era defined by data, complexity, and change.

“Bayesian thinking,” she concluded, “teaches us to learn from what we already know, and to keep adjusting our understanding as new evidence arrives. That’s not only good science; it’s good sense.”

Throughout her address, Prof Raubenheimer expressed gratitude to the mentors and collaborators who shaped her journey – among them, Professors Sarah Radloff, Tertius de Wet, Abrie van der Merwe, Tom Mazzuchi, Refik Soyer and the late Professors Daan De Waal and Nozer Singpurwalla, as well as deans, colleagues, and students whose support continues to guide her teaching, community initiatives and research.

Whether in research, engineering, or policy, the professor’s message was unmistakable: the future belongs to those who can think critically, reason under uncertainty, and keep learning from the evidence before them.