Jiangang Xia is an enterprising professor at the University of Nebraska who, among many other things, teaches into China. He alerted me some years ago to the difficulty his students in China had accessing my videos because YouTube was blocked for them. So I mounted the videos at this OSF site.

As another enterprising step, Jiangang has recently been making a number of posts to LinkedIn. Below is one of these:

Rethinking Quantitative Reasoning in Educational Research (4)

Jiangang Xia

Associate Professor of Educational Administration at University of Nebraska–Lincoln

January 13, 2026

Geoff Cumming and the New Statistics: Estimation as a Way of Thinking

Some years only reveal their importance in hindsight. For me—and for statistics reform—2014 was one of those years.

That was the year I had just begun my academic career at the University of Nebraska–Lincoln. It was also the year Rex Kline visited UNL and delivered his keynote, “Hello, Statistics Reform.” (for Kline’s talk, please see my Post 2 for more details). At the time, I had no idea how much that visit would eventually shape my thinking, teaching, and research.

What I did not know then—and only came to appreciate much later—is that 2014 was also the year Geoff Cumming delivered his workshop “The New Statistics: Estimation and Research Integrity” at the APS Annual Convention in San Francisco.

I was completely unaware of that workshop.

In fact, I would remain largely unaware of Cumming’s work for several more years—even though it quietly passed through my academic life more than once.

Missed encounters (seen only in hindsight)

While preparing this post, I did something simple: I searched Geoff Cumming’s name in my old emails. That’s when I realized how often his work had crossed my path without fully registering.

In 2016, I co-chaired a dissertation in which the student cited Cumming’s influential article “The New Statistics: Why and How” (2014). At the time, neither of us fully grasped what the article was really asking us to reconsider. Although reform language appeared, the dissertation still relied on phrases like “marginally significant” for p values above .05—an indication that NHST logic remained firmly in place. Estimation had been encountered, but not yet learned as a way of thinking.

In 2017, I received an email from Routledge inviting faculty to request a desk copy of Introduction to the New Statistics. I didn’t request it. Another missed opportunity—one I only recognize now, looking backward.

These weren’t personal oversights so much as reflections of how deeply NHST was normalized in our training. Reform ideas were present, but the infrastructure for learning them—how to teach them and how to use them—was still thin.

From critique to alternative

It wasn’t until 2019, through Rex Kline’s work, that the larger picture finally came into focus for me. From there, I traced the reform movement backward—and Geoff Cumming’s role became unmistakable.

Cumming and his colleagues were not simply extending Jacob Cohen’s critique of null hypothesis significance testing. Cohen had already shown, powerfully, why NHST was flawed and had pointed toward alternatives. He also played a central role in the APA Task Force on Statistical Inference, whose report was released in 1999.

Unfortunately, Cohen passed away in 1998, before that work could be fully carried forward. When the APA’s 5th edition was published in 2001, many of the reform ideas were only partially adopted, and everyday research norms remained largely unchanged.

What Cumming did next was different.

Through decades of scholarship—culminating in The New Statistics—he articulated a coherent alternative centered on estimation rather than binary decisions, emphasizing effect sizes, confidence intervals, precision, uncertainty, and cumulative evidence. By the New Statistics, Cumming meant an estimation-centered approach to inference—focusing on effect sizes, confidence intervals, and uncertainty, and on combining evidence across studies through meta-analysis—rather than making binary decisions based on p values alone.

From alternative to institutionalization and teaching

That work did not remain theoretical.

In 2008, Cumming was invited to join the small working group responsible for statistical reporting standards in the APA’s 6th edition, where he was a driving force behind the requirement that effect sizes and confidence intervals be reported for every research question—helping move estimation from an optional supplement to a core reporting standard.

Just as importantly, Cumming took the initiative to teach this alternative—by writing new articles (2014), new textbooks (2013, 2017, 2024), offering workshops (2014 APS), and developing demonstrations (ESCI) aimed at broad audiences, not just methodologists.

Why experience matters in teaching reform

Along the way, Cumming recognized something more fundamental: logical arguments alone were not enough.

As he later reflected, perhaps NHST had become “the researcher’s heroin—an addiction impervious to reason.”

If that was true, persuasion would require more than explanation. It would require experience.

This insight shaped his teaching. Rather than debating p values in the abstract, Cumming showed researchers—often viscerally—how unstable they are through demonstrations such as the dance of the p values, p intervals, and significance roulette. The goal was not just to convince the mind, but to engage the gut—to help researchers feel uncertainty rather than deny it.

A decade later, the field itself began to catch up. In 2024, Cumming’s 2014 article “The New Statistics: Why and How” received SAGE’s 10-Year Impact Award, recognizing research whose influence endures well beyond the standard citation window. The award was a reminder that reform ideas are often understood slowly—resisted early, adopted unevenly, and acknowledged only after they have quietly reshaped teaching and research norms.

Full circle: from missed encounters to transformed practices

What changed for me after 2019 was not just what I read—but how I taught, mentored, and conducted research.

In 2022, I shared Cumming’s 2014 article with my doctoral advisee Amanda. Her dissertation became the first I supervised to fully abandon significance language, adopting estimation-based interpretation throughout. That same year, a manuscript my student Cailen and I submitted—published in 2023—explicitly drew on Cohen, Kline, Cumming, and the ASA (2016) statement. It was my first research article grounded fully in estimation thinking.

And in a quiet but meaningful full circle, when I needed materials for my 2024 summer teaching in China, Geoff Cumming himself shared his 2014 APS workshop videos with me—materials I have since used both internationally and in my quantitative methods courses at UNL.

Looking back now, the reform was happening all around me in 2014.

I just wasn’t ready to see it.

Perhaps that is how intellectual change often works—not as a single revelation, but as a series of missed encounters that eventually align. And perhaps genuine reform requires not only better arguments, but better ways of helping researchers experience uncertainty.

For readers who want to explore further

• Cumming’s 2014 APS Workshop (six videos and slides)
• Significance Roulette YouTube Videos (Significance Roulette 1) (Significance Roulette 2)
• Browser-based Significance Roulette (“The p Value Casino”), developed by Bradley Dean as part of esci web

(All materials shared with permission; enormous credit to Geoff Cumming, Bradley Dean, and Robert Calin-Jageman.)

Dear colleagues,

• When did you first encounter Geoff Cumming’s work—if at all?
• Have you seen estimation treated as an add-on, rather than a way of thinking?
• What important ideas did you meet early, but only understand much later?

#Cumming #NewStatistics #EstimationThinking #QuantitativeMethods #EducationalResearch

Thank you Jiangang!

Geoff