Nick Garrett

Nick Garrett

Department of Biostatistics and Epidemiology, Auckland University of Technology
In this article, I describe my career as a New Zealand statistician specialising in gambling and gambling-harm research. I explain how my work focuses on using rigorous data analysis, longitudinal studies, and population-level methods to understand how gambling risk and harm change over time. The article outlines my role in national gambling research projects, particularly the New Zealand National Gambling Study, and my collaboration with multidisciplinary research teams. I also discuss the importance of statistical integrity, transparent reporting, and public-health approaches to gambling harm. Overall, the text presents my professional journey, research philosophy, key contributions, and commitment to evidence-based policy and harm reduction.

My work, my path, and why I study gambling harm

I’m Nick Garrett, a statistician and applied researcher based in New Zealand. My professional life has been shaped by one central question: how do we use rigorous data analysis to understand gambling behaviour and reduce gambling-related harm at a population level?

I’ve spent much of my career working at the intersection of statistics, public health, and policy-relevant research, particularly through large national studies of gambling participation and harm. I’m not a clinician, and I’m not a lobbyist. My role has been to make the data speak clearly—to ensure that evidence is measured carefully, analysed transparently, and communicated in a way that decision-makers and communities can actually use.

This article is my attempt to describe that work in my own words: where I come from, how I approach gambling research, who I’ve worked with, and why statistical integrity matters so much in a field that often attracts strong opinions.

Early interests: statistics as a tool for social understanding

I was drawn to statistics not because I enjoyed equations for their own sake, but because statistics is one of the few tools that allows us to describe complex social realities honestly. Human behaviour is messy. Gambling behaviour is especially so. People don’t gamble in neat categories; they move in and out of risk, change products, respond to life events, and adapt to policy and availability.

From early in my training, I was interested in applied statistics—methods that are robust enough for real-world data, including:

  • survey sampling and weighting,
  • longitudinal analysis,
  • missing data and attrition,
  • risk transition modelling,
  • uncertainty and confidence intervals in public reporting.

These interests naturally aligned with public-health research, where decisions affect entire populations and measurement errors can have real consequences.

How I became involved in gambling research

My involvement in gambling research grew through work with multidisciplinary teams studying population-level gambling behaviour in New Zealand. Gambling is a topic that sits at the crossroads of economics, psychology, sociology, and health. For statisticians, it presents unique challenges:

  • gambling participation is unevenly distributed,
  • a small proportion of players account for a large share of expenditure,
  • harm is multi-dimensional and often under-reported,
  • and behaviour changes over time.

I became closely involved in the New Zealand National Gambling Study (NGS)—a large, longitudinal research programme designed to track gambling participation, risk status, and harm across multiple waves. My role focused on study design, statistical modelling, and interpretation of results, ensuring that findings were both technically sound and policy-relevant.

Working with longitudinal data: why it matters

Cross-sectional surveys tell us what things look like at one point in time. Longitudinal studies tell us how people change.

In gambling research, that distinction is crucial. Many misconceptions come from assuming that people who experience harm are a fixed group. Longitudinal analysis shows something very different:

  • people move into and out of risk categories,
  • harm can appear without meeting strict diagnostic thresholds,
  • life events (financial stress, relationship changes, health issues) matter,
  • exposure and availability shape trajectories.

My work has focused on modelling these transitions accurately—estimating how many people shift between risk states, and under what conditions. This kind of analysis helps policymakers understand that prevention is not just about treating a small group of “problem gamblers,” but about reducing risk across the whole population.

Collaboration as a core principle

I have never worked alone. Gambling research in New Zealand has been a team effort, involving psychologists, public-health researchers, clinicians, qualitative researchers, and policy specialists. My statistical work has supported and complemented contributions from colleagues such as Max Abbott, Maria Bellringer, Stuart Mundy-McPherson, and others involved in national gambling research programmes.

In these collaborations, my responsibility has often been to:

  • design analytic strategies,
  • test assumptions behind reported figures,
  • stress-test conclusions against alternative models,
  • explain uncertainty clearly,
  • and ensure transparency in reporting.

I see this role as both technical and ethical. When research informs public debate, clarity and honesty about uncertainty are non-negotiable.

Measuring gambling harm beyond diagnoses

One of the most important shifts I’ve witnessed is the move away from treating gambling harm solely as a clinical diagnosis. Statistically, this shift required new approaches.

Harm is not binary. It exists on a spectrum:

  • financial stress,
  • reduced wellbeing,
  • relationship conflict,
  • work or study disruption,
  • emotional distress.

My work has contributed to analyses that capture harm across this spectrum, rather than counting only those who meet diagnostic thresholds. From a statistical perspective, this means:

  • working with composite harm measures,
  • modelling low-to-moderate harm prevalence,
  • analysing cumulative impacts across households and communities.

This broader measurement approach aligns much more closely with a public-health framing of gambling harm.

Integrity in data and reporting

One thing I’ve learned is that gambling data is often misunderstood or misused—sometimes unintentionally, sometimes selectively. Percentages are quoted without denominators; point estimates are reported without confidence intervals; trends are inferred from short time spans.

My professional commitment has been to guard against these errors. In practice, that means:

  • insisting on proper weighting and calibration of survey samples,
  • clearly distinguishing correlation from causation,
  • avoiding over-interpretation of small sub-group results,
  • documenting methods so analyses can be scrutinised and replicated.

Good statistics don’t make headlines louder—but they make conclusions more trustworthy.

Institutional roles and workplaces

Most of my work has been conducted within academic and research institutions in New Zealand, particularly through Auckland University of Technology (AUT) and affiliated research centres focused on gambling and addictions research.

My institutional roles have combined:

  • applied statistical analysis,
  • co-authorship on national reports,
  • methodological consultation,
  • and contribution to research design and evaluation.

I’ve worked across multiple funded projects, often commissioned by public agencies, where independence and methodological rigor were essential.

Why this work matters to me

Gambling research is sometimes framed as abstract or controversial. For me, it’s deeply practical. Statistics shape:

  • how harm is recognised,
  • which interventions are funded,
  • how policies are evaluated,
  • and whose experiences are counted.

When numbers are wrong—or oversimplified—real people are affected. That’s why I’ve stayed in this field. I believe that careful measurement is a form of harm prevention.

Selected publications & research contributions

YearTitle / ContributionTypeLink
2017Gambling and Gambling Harm in New Zealand: A 28-Year Case Study (co-author)Research article (PDF)ResearchGate
2015New Zealand National Gambling Study: Wave analyses (statistical modelling)Government research reportAUT GARC
2014Transitions in gambling risk status (longitudinal analysis)Technical reportNZ Ministry of Health
2019Qualitative and quantitative integration in gambling harm research (co-author)Research reportAUT PHMHRI

Workplaces & Professional Roles (Interactive)

PeriodInstitutionRoleFocus
2010s–presentAuckland University of Technology (AUT)Statistician / ResearcherGambling harm, population studies
2012–2016New Zealand National Gambling StudyStatistical analystLongitudinal modelling, risk transitions
Various projectsPublic health research teamsMethodological advisorSurvey design, data integrity

Research Focus Areas (Interactive)

Area ▲▼Description ▲▼
Longitudinal analysisTracking changes in gambling risk and harm over time
Population statisticsEstimating prevalence and harm distribution
Risk transitionsMovement between low, moderate, and high-risk states
Survey methodologySampling, weighting, and bias reduction

If there’s one message I’d leave readers with, it’s this: **good gambling policy starts with good data**. Statistics won’t solve gambling harm on their own, but without reliable measurement, every other response is built on sand. My work has been about making sure the evidence is strong enough to support decisions that genuinely reduce harm—quietly, carefully, and honestly.

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