AI systems are excellent at sounding certain. Statistics asks whether they should.
That question—how much confidence we should place in predictions, recommendations, and data-informed decisions—is becoming increasingly important in a world shaped by algorithms. Every day, people rely on systems built on statistical reasoning, often without realizing it. Weather forecasts estimate the probability of rain. GPS apps predict traffic patterns and reroute drivers in real time. Streaming services recommend movies based on behavioral models. Doctors evaluate risks and treatment outcomes using statistical evidence. AI systems generate answers by identifying patterns in enormous datasets. Modern life, in other words, quietly runs on probability.
But probability is uncomfortable for people; most of us prefer certainty. Statistics, on the other hand, deals in likelihoods, confidence intervals, margins of error, and informed estimates about an uncertain world.
A recent National Academies report, Frontiers of Statistics in Science and Engineering: 2035 and Beyond, argues that statistics will become increasingly essential in the AI era precisely because AI systems alone cannot answer some of society’s most important questions. Is a prediction reliable? How biased is a model? How uncertain are the results? Statistical science provides many of the tools researchers use to evaluate trustworthiness and reliability. As the report states, “Statistics is the science through which data become wisdom.” In other words, AI may generate answers, but statistics helps determine whether those answers deserve our confidence.
The report reflects a broader shift already underway across science, engineering, medicine, and public life. Statistics is no longer a specialized discipline quietly supporting research from behind the scenes. It has become part of the infrastructure of modern society, influencing fields as varied as climate science, forensic analysis, transportation, manufacturing, healthcare, city planning, finance, and public policy.
Most people do not picture statisticians helping design safer road systems, analyzing forensic evidence, improving public health responses, or helping cities make decisions about housing and transportation. Popular stereotypes still place statisticians in difficult math classes, spreadsheets, or charts.
Part of the challenge is that statistical work often disappears into the systems it improves. People see the forecast, not the probabilistic model underneath it. They see the recommendation, not the uncertainty calculations behind it. They see the prediction, not the statistical reasoning supporting the conclusion.
Increasingly, however, statisticians are beginning to step more visibly into public conversation.
The American Statistical Association’s Telling Our Stories video series highlights statisticians working in fields many people would never immediately associate with the profession. One statistician may apply statistical methods to forensic science. Another may help cities analyze infrastructure and transportation data. Others work in public service, healthcare, environmental science, manufacturing, or public policy. The series pulls back the curtain on work that shapes everyday life while rarely drawing public attention to itself.
The ASA’s Dionne Price Public Lecture reflects a similar effort to connect statistical thinking to broader public issues. Named in honor of former ASA president and pioneering statistician Dionne Price, the lecture series invites speakers to explore topics with public relevance and demonstrate how statistical reasoning helps society better understand complex problems.
Together, these initiatives reflect a broader evolution within the field. For decades, statisticians largely communicated with other specialists, but as data-informed systems become more influential in daily life, statistical literacy increasingly matters beyond professional circles. Understanding concepts such as uncertainty, risk, evidence, and probability is becoming part of navigating modern society. Questions once confined to research labs now shape public conversations.
That creates a new challenge for the profession: not simply advancing statistical science but helping the public understand why statistical thinking matters in the first place.
Ultimately, statistics is about making decisions in situations where certainty is impossible. It is about asking how we know what we know, how confident we should be in the answers, and what risks remain hidden inside the data. For years, that work quietly powered discoveries from behind the curtain. Now, as AI systems generate more predictions and decisions at unprecedented speed, statistics is beginning to move into public view—not because the discipline suddenly changed, but because modern life increasingly depends on understanding uncertainty well.
Valerie Nirala
ASA Publications Strategist
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