Welcome to my blog pages.
This is where I post whatever is on my mind. Usually it's related to statistics, analysis and research and may reference some of the courses that I currently teach, or the work that I am currently involved in.
If you have any ideas / topics for future blogs then please let me know.
This article takes aim at the obsession with identifying AI-generated writing based on shallow markers like punctuation and phrasing. Starting with Dickens' famous opening to A Tale of Two Cities, the author points out the absurdity of using elements like an em dash to discredit a piece of writing. Instead of reading for meaning, too many people scan for signs of machine involvement and dismiss the content without engaging with the argument at all.
The writer expresses growing frustration with this surface-level culture, especially on platforms like LinkedIn. Thoughtful posts are ignored while tired criticisms of AI writing get amplified. People who put genuine time and care into their work often have to change how they write just to avoid being labelled as artificial. It’s exhausting, and it’s silencing the voices that actually have something to say.
The real issue isn’t how something was written, but whether the ideas are worth engaging with. This piece calls for a shift in attention; towards substance, nuance, and reflection. If we keep rewarding noise and ignoring care, we’ll be left with nothing but shallow content. If this resonates you will ant to read the full article. It is a far, far better thing you'll read, than you have read before.
Artificial intelligence is often described as “thinking,” but that label may be more confusing than helpful. The real issue is not whether AI is getting smarter. It is that we have never clearly defined what “thinking” actually means. The words we reach for come from a time before AI existed. They carry assumptions that do not map well to how these systems work.
Take Descartes and his famous line, Cogito ergo sum, which means "I think, therefore I am." He was not just making a philosophical point. He was laying a foundation for certainty and using it to support his argument for the existence of God. Thought, for Descartes, was proof of being. It was tied to awareness, doubt, and a divine framework. That kind of thinking has little to do with what machines are doing when they predict the next word in a sentence.
And yet, the outputs from these systems can seem intelligent. They answer questions, solve problems, and mimic human reasoning. But calling that “thinking” risks misleading people. The danger is not the AI itself. It is that we are using the wrong language to describe what it does. If you are curious about how our old definitions are falling short, and why that matters, read the full article.
We often trust our perceptions, models and past experiences to make decisions, in life, in business, and in science. But this article challenges that instinct, showing how our thinking can be shaped by hidden assumptions and biases. Drawing on the principles of the scientific method, it explores how habits like scepticism, empiricism, reductionism and determinism can help us test what we believe rather than just accept it.
The parallels with business are clear: questioning accepted strategies, being honest about weak evidence, and resisting the urge to force everything into neat narratives are just as relevant to running a company as they are to running an experiment. The article draws comparisons between scientific theories such as the Big Bang or evolution and business frameworks, not to discredit them, but to show how both need constant testing and revision.
It is a call for intellectual humility. A reminder that good decisions often come from not being too sure. Whether you're leading a team or launching a product, adopting the mindset of a scientist, curious, methodical, ready to revise, can help you avoid costly errors. If that sounds useful, the full article is well worth a read.
AI can seem slick and capable, but trusting it blindly is a bit like handing the keys to a business over to a teenager called Harry. Confident, unpredictable, and not always honest about what it's done or why, AI systems can behave just like adolescents, especially when left unsupervised. The blog explores this comparison in a light, relatable way that’s grounded in real risks and responsibilities.
By walking through a day in the life of Harry, a poorly teen making bad choices thanks to a fever, the article draws a parallel with AI models that go off track. The parent, like a responsible business, notices the signs, steps in, seeks expert help, and keeps Harry away from others to prevent more harm. The piece reminds us that you can’t expect good decisions from either a sick teenager or a malfunctioning AI.
It also reinforces the need for oversight, structure, clear boundaries, and the ability to pause or escalate when things go wrong. Someone has to be accountable, someone has to be watching. If any of this sounds familiar or raises a few questions about your own use of AI, it’s worth taking five minutes to read the full article.
AI systems don’t all work the same way. Some generate content, others make predictions, some act independently, and a few stick rigidly to rules. Each model has its own logic, strengths, and blind spots. When combined, they often work at cross purposes, misunderstanding each other’s output or turning small errors into bigger ones. That’s the risk when different types of AI are thrown together without planning. Things start to behave unpredictably.
Adaptive AI is slightly different. It doesn’t invent or forecast. It learns from immediate context, just what happened a moment ago, and reshapes its behaviour in real time. That can be helpful, even charming, when it gets something right. But if it misreads the room or takes a single action as a preference, it adapts in ways that feel personal but are actually based on guesswork. It’s not malicious, just confident with too little to go on.
This article introduces Jamie, a human version of Adaptive AI, who tries to help based only on what he saw at a party the night before. Through that lens, we explore how short-term learning can quietly cause longer-term confusion. If you’ve ever wondered why your tech suddenly acts like it knows you too well, or keeps adjusting just when you’d figured it out, Jamie might be the reason.
Read the full article to meet Jamie and the rest of the AI family, and see what happens when one of them tries to run breakfast.
We’ve long leaned on the idea that what we see and hear is real. A photograph, a video clip, a voice on tape—these were once enough to settle arguments and confirm events. But with AI now able to create realistic faces, voices, and footage from scratch, the old rules no longer apply. What looks and sounds convincing might be entirely synthetic, and most of us aren’t equipped to tell the difference.
It’s not just politics or crime scenes. AI is writing music, generating news stories, producing deepfake interviews, even mimicking dead celebrities. Some of it feels creative or clever, but it also clouds authorship, truth, and accountability. As more of this content circulates, we need better tools for spotting fakes, new ways of confirming what’s genuine, and a stronger instinct to pause before we believe what we’re shown.
This article looks at real-world examples, from political deepfakes to AI-generated Spotify tracks, and asks what we can do next. If you’ve ever wondered whether reality is starting to slip, or if the phrase “perception is reality” still holds up, this piece is worth your time. Have a read and see where you land.
Shakespeare said a rose would smell just as sweet by any other name. But in artificial intelligence, names matter more than ever. The term "AI" is now used so broadly that it often misrepresents what a system can actually do. From simple automation to complex machine learning, calling something "AI-powered" can create false expectations.
Many products marketed as AI are really just basic software following preset rules. When companies use terms like "smart," "autonomous," or "agentic," people expect systems that understand, adapt, and make independent decisions. But most AI tools simply analyze data patterns without real awareness or judgment.
This mismatch leads to trust issues, legal risks, and disappointment. Examples include tools labeled as "robot judges" that only assist human decision-making, or hiring algorithms that replicate existing biases but are called "intelligent." The language used shapes how people interact with these tools and who is held responsible when things go wrong.
If you work with AI or use it, it is important to use clear and honest language. Describe what your system actually does instead of what you hope it does. And as a user, be cautious of overhyped claims.
Names are not neutral. In AI, they influence perception, trust, and accountability.
To learn more about why naming matters so much in AI, read the full article.
Lately, I’ve noticed something odd on LinkedIn. Thoughtful, well-crafted posts about AI, data, leadership, and culture often get little engagement, even when people are reading them. Meanwhile, quick personal updates like job announcements or award photos get lots of likes, comments, and shares.
This isn’t just about what’s popular. LinkedIn’s algorithm favours early reactions, so many valuable posts quietly disappear. That’s why reposting matters more than most realize. Sharing someone else’s content keeps important ideas alive and helps them reach new audiences, especially for those of us without huge followings.
If you find something meaningful or thought-provoking, don’t just read and move on. Give it a second life by reposting. It’s a small action that can make a big difference in how ideas spread and how conversations develop.
Want to understand why reposting matters so much, and how it shapes the quality of content we see? Read the full article for deeper insights.
AI is often talked about as a single clever tool, but it’s more like a family—full of different personalities that don’t always get along or behave as expected. Imagine hosting a chaotic family gathering, where each relative represents a type of AI with its own quirks and challenges.
Cousin Steve (Generative AI): Creative and enthusiastic but prone to making things up. Great for ideas but needs supervision and fact-checking.
Mum and Dad (Predictive AI): Always guessing what comes next based on past data. Useful, but their predictions aren’t guarantees and can reflect bias.
Uncle Rob (Large Language Models): Fluent and confident talker who sounds convincing but doesn’t always check facts. Best used with boundaries and oversight.
Older Sibling (Agentic AI): Takes initiative and acts independently. Powerful but can cause chaos if unchecked, so limits and checkpoints are essential.
Grandad (Symbolic AI): Rule-based and predictable, but inflexible when things don’t fit neat categories. Best in well-understood, stable environments.
Most AI setups today are hybrids, mixing these “family members,” which creates complexity and potential conflicts that require careful management.
For a full breakdown of each AI type, what to expect, and how to prepare your “party” for success, check out the complete article. It’s a must-read for anyone working with AI today.
AI sparks all kinds of reactions: excitement, fear, or outright denial. Among these voices are the doomsayers, those warning that AI could bring catastrophic collapse. Like ancient prophets sounding the alarm, they paint vivid pictures of AI taking over work, society, or even humanity itself.
But their predictions deserve a closer look. While some claims are extreme, like AI wiping out all jobs or suddenly becoming sentient, they do not align with current realities. Instead, AI is reshaping how we work, automating some tasks but also creating new roles. And today’s AI systems, no matter how clever, are not self-aware; they are just pattern-matching machines.
Where the doomsayers gain traction is in the real, present-day risks: biased algorithms locking people out of opportunities, flawed medical tools missing diagnoses, deepfakes undermining trust. These are not sci-fi fears; they are happening now, quietly and sometimes dangerously.
The challenge is clear. AI is not just another tech upgrade. It changes decision-making and impacts real lives, so organizations must slow down, ask tough questions, and build trust from the start. Oversight, transparency, and accountability cannot be afterthoughts.
Doomsayers may exaggerate, but they also push us to be vigilant, thoughtful, and responsible. Ignoring them risks repeating mistakes. Listening and acting wisely might just be how we turn their warnings into progress.
For a deeper dive into the fears, facts, and future of AI, the full article is well worth your time.
This article looks at what happens when belief is mistaken for truth, and how relying too much on perception can lead to serious mistakes. From healthcare to the workplace, the piece shows how decisions often rest on what feels true rather than what is actually supported by evidence. Misdiagnoses happen, people are promoted for their visibility rather than their value, and entire systems are shaped around assumptions that go unchecked for too long.
The article uses powerful examples, including the long-standing misconceptions about Chronic Fatigue Syndrome and the careless overuse of antibiotics, to show how these patterns play out in real life. It also explores how performance is measured in many organisations, often in ways that reinforce outdated ideas and reward style over substance. These stories expose the quiet damage caused when belief is given more weight than reality.
If you have ever wondered why poor decisions persist even when the evidence says otherwise, this article will give you a clear and thoughtful perspective. It challenges comfortable assumptions and invites the reader to think more critically about what they accept as true. The full piece is well worth reading for anyone who wants to better understand the hidden influence of belief in everyday decisions.
Artificial intelligence is being used to paint portraits, write stories, generate music, and simulate entire conversations. That’s the side most people see. But there's another kind—quieter, less visible—that claims to know what you're likely to do next. It predicts whether you’ll succeed, whether you’re a risk, whether you deserve a loan, a job, or a second chance.
This article explores the difference between creative, generative AI and its more quietly influential cousin, predictive AI. Through examples from Blade Runner, Gattaca, Minority Report, The Matrix, and more, it shows how fiction has been warning us for years about what happens when machines aren’t just tools, but judges of human potential.
The systems we trust to be neutral often aren’t. They don’t just make predictions. They shape the outcomes. They create feedback loops. They define futures people haven’t had the chance to live.
If you’re worried about rogue machines, you’re looking in the wrong direction. The real threat may be the systems that seem calm, logical, and fair—yet gradually decide who you are allowed to be.
Read the full article to see how the most powerful AI isn’t the one that acts, but the one that assumes.
Bias can easily slip into AI systems through the data used for training or during development choices. Several common types of bias are explained, such as selection bias caused by limited or unrepresentative data, and labelling bias that comes from inconsistent human categorisation. Recognising these helps understand why AI sometimes produces unfair or flawed results.
Real-world examples show how bias impacts important decisions. A hiring algorithm unfairly downgraded applications referencing women’s colleges, while a legal risk assessment tool gave inconsistent scores due to biased historical data. Cases in finance and healthcare reveal how bias can affect credit limits and patient prioritisation, illustrating the real consequences of unchecked bias in AI.
Practical steps are outlined for tackling bias, including scrutinising data carefully, testing models with unusual scenarios, and involving diverse perspectives in development. Keeping thorough records and proactively searching for issues can prevent problems before systems go live. Reading the full article will provide a clearer picture of bias in AI and useful approaches to manage it effectively.
Data collection and analysis often take all the attention, but the moment when data becomes outdated or irrelevant is just as important. Holding on to old information can create risks that go beyond just wasted storage. Forgotten files and duplicated records can become a liability, especially if they contain sensitive personal or business details. Over time, the value of data fades while the risks remain or even grow.
The article explores what happens when obsolete data isn’t properly deleted. It uses the example of a spy who keeps every piece of intelligence after a mission, only to have that data become a vulnerability. In reality, old data left unsecured can be exploited, misused, or accidentally exposed. When AI systems consume outdated or biased data, they make confidently wrong decisions that cause real damage. Regulatory requirements like GDPR make clear that data should not be kept longer than necessary, and failure to comply carries serious consequences.
Practical advice is offered on how to handle data lifecycle responsibly: setting clear expiry rules, automating deletion, respecting consent, conducting audits, and building data literacy across teams. Treating data deletion with the same care as data collection helps organisations reduce risk and build trust. There is real value in knowing when to let data go. To understand the risks and learn how to manage data more effectively, the full article is worth a read.
Data often looks simple at first, but it can be full of hidden biases and flaws. Legal compliance sets the minimum standards, but it does not guarantee fairness or transparency. Without asking the tough questions about where data comes from and how it is used, decisions based on that data risk causing real harm.
AI systems now play a major role in decisions about jobs, healthcare, and finances. Yet many operate as “black boxes,” making it hard to understand or challenge their outcomes. If the data behind them is biased or incomplete, those systems only amplify existing inequalities.
A healthy data culture means being aware of context, questioning assumptions, and treating ethics as a central part of every data process. Transparency and responsibility are key to building trust and avoiding unintended consequences.
If you want to understand why ethics must come first in data and AI, this article is well worth a read.
We all rely on personal experience and gut feeling to make decisions, but when those choices affect many people, mistaking perception for reality becomes risky. Without solid data and the skills to interpret it, decisions are prone to bias and assumptions, which can lead to misguided policies and misallocated resources.
Data literacy is crucial across all levels of an organisation, not just for analysts. When people don’t understand the data’s context or limitations, they fill the gaps with their own views, which may be narrow or biased. Building a workforce comfortable asking tough questions about data helps prevent decisions based on incomplete or misleading information.
The gap between perception and reality can be wide, especially in complex issues like social mobility or homelessness. Data doesn’t replace experience but challenges it, offering a broader, more accurate picture. When more people understand data and can question it effectively, decisions become fairer and more effective.
If you want to explore why data literacy matters so much and how it can help organisations avoid costly mistakes, this article is worth a read.
We often hear that "perception is reality," but in data analysis, this idea can be misleading. Our personal views are shaped by biases and experiences, while reality is independent and fixed. When interpreting data, it’s easy to mistake statistical results for absolute truths, yet measures like p values, confidence intervals, and study power all come with uncertainties that challenge our initial impressions.
Statistics like p-values can indicate if a result is unlikely to be random, but they don’t tell us how meaningful the effect actually is. Tools such as sensitivity and specificity help assess tests’ reliability but still allow for errors: both false positives and false negatives. These limitations show why numbers should be understood as clues rather than certainties, and why careful interpretation beyond surface-level results is essential.
The gap between what data seems to say and what it truly means matters a great deal, especially in fields like health research. Overconfidence in statistical significance without considering context can lead to overstated conclusions or missed insights. Accepting uncertainty and approaching analysis with curiosity and scepticism helps reveal a clearer, more nuanced picture.
If you want to dive deeper into how perception and reality clash in statistics, and why understanding this gap matters, this article offers useful examples and practical reflections.
The data environment has rapidly evolved with an explosion of data and analytical tools. Many organisations now use programming languages like Python and visualisation platforms such as Power BI to explore data. However, there is a growing problem where businesses value tool skills more than a deep understanding of statistical and analytical techniques. This focus on tools over technique can lead to shallow analysis and flawed decisions.
In other fields, expertise is judged by skill rather than the tools used, yet data analysis often reverses this. Just as a surgeon is chosen for their knowledge rather than their scalpel, and a musician for their skill rather than their guitar, businesses must prioritise strong analytical techniques over simply knowing how to operate software. Without this foundation, organisations risk misinterpreting data and missing chances to innovate or compete effectively.
If you want to learn why focusing on analytical skill instead of just tools can protect your business and improve decision-making, take a moment to read the full article. It explains how investing in data literacy and expertise can make all the difference in today’s data-driven world.
Data storytelling is an often overlooked skill that can transform how businesses present and use their data. Rather than simply showing numbers and charts, data storytelling combines analysis with narrative and visuals to create engaging, clear, and meaningful stories. This approach helps decision-makers understand insights better and motivates action, turning complex data into something relatable and memorable.
Many organisations invest heavily in training analysts to extract data but fail to support them in communicating those findings effectively. Without a compelling story to frame the data, important insights risk being misunderstood or ignored. By adopting data storytelling practices, companies can bridge the gap between raw data and strategic decisions, encouraging collaboration and fostering a culture where data truly informs business direction.
If you want to see how data storytelling can boost your organisation’s impact, improve communication, and drive positive outcomes, take a moment to read the full article. It explains why storytelling is not just a presentation tool but a vital skill for modern businesses aiming to thrive in a competitive world.
Data literacy is essential for organisations to fully benefit from the data they collect. While many invest heavily in technology and technical skills, the real value comes from employees who understand the full story behind the data: where it comes from, its limitations, and what it actually means. Without this deeper understanding, organisations risk misinterpreting their data, making poor decisions, and missing important insights.
A common problem in many companies is the siloed approach to data management, where different teams only see part of the process and fail to grasp the bigger picture. This fragmentation can lead to confusion, conflicting interpretations, and even misuse of data. Developing data literacy across the entire workforce helps to break down these barriers, enabling clearer communication, stronger data governance, and better alignment between departments.
If you want to learn why data literacy is more than just technical know-how and how it underpins ethical, accurate, and effective use of data in organisations, the full article is worth a read. It explains how a data-literate workforce supports better decision-making and protects a company’s reputation in today’s digital world.
This piece reflects on the idea of seeing situations from other people’s perspectives, starting with a childhood story about hand-me-down clothes and shoes. It explores how early feelings of unfairness shaped the author’s understanding of empathy and the importance of considering others’ circumstances. The writer connects this personal insight to broader issues like workplace stress, management challenges, and discrimination.
The article stresses that empathy is essential in leadership and daily interactions. Treating everyone equally isn’t enough because people have different needs, experiences, and backgrounds. Understanding these differences helps prevent inappropriate discrimination and improves how we relate to others. This approach also applies to data analysis, where knowing the context behind the numbers and the people involved can make the results more useful and accurate.
Finally, the blog encourages readers to pause before judging others or reacting negatively. It suggests practicing empathy regularly, not just in difficult moments, and appreciating that people’s actions often reflect unseen pressures. The message is that we all benefit from stepping into someone else’s shoes and seeing the world from their view.
If this resonates with you, I recommend reading the full article to explore these ideas in more depth.
The article steps away from usual topics like data and statistics to reflect on the culture around us, especially in light of the Covid pandemic. It questions whether causing harm to others can ever be justified for personal or organizational gain. Drawing parallels with phrases like “collateral damage” and “for the good of medical science,” it argues that many still accept hurting people if it benefits a larger goal, such as profits or progress, even when that harm is avoidable or unnecessary.
The post highlights workplace bullying as a serious issue often dismissed or tolerated under the guise of company culture or business needs. It explains how toxic environments wear down individuals, leading to emotional and mental harm, and sometimes even bonding victims to their abusers. The author stresses that bullying should be treated like abuse, not a normal part of work life, and that employers and HR must do more to protect employees rather than just defending the company.
Finally, the piece calls for us to stop sugarcoating workplace mistreatment with soft language and to recognize it for what it truly is: abuse and violence. It argues that if domestic abuse is criminal, workplace abuse should be too. The author urges everyone to remember basic human decency, treat others with respect and kindness, and not settle for anything less.
If this strikes a chord, reading the full post will give you a clearer view on why calling out workplace abuse honestly matters.
Data availability and use in health and social care have evolved significantly since the early 1990s, moving from limited and anecdotal information to vast, complex datasets drawn from many sources. This expansion goes beyond healthcare to social media, finance, and more. Alongside this growth comes a need to shift the mindset about how data is managed and analysed.
A key issue is the importance of having skilled people handle data, much like companies employ experts for financial matters. Despite this, data analysis is often treated as something anyone can do, which carries risks. Ethical concerns arise because following the law is not enough; questions about consent, purpose, skills, risks, and impact must be carefully considered before analysing data.
The old justification of pursuing data use or research “for the good of medical science” is compared to outdated ideas about acceptable collateral damage. Ethics should be central in data use, with proper training and restricted access to sensitive information. Ethical considerations need to become as routine as technical skills for those working with data.
If you want to explore these ideas further, reading the full article is worth your time.
The phrase "Ask a silly question, get a silly answer" often discourages people from seeking clarity, especially in adult life. This mindset can make individuals hesitant to question experts or complex information, leading to misunderstandings or blind acceptance of what they are told. The author shares a personal experience of speaking up in a meeting only to discover that nobody really understood the jargon-filled discussion, showing how important it is to ask questions—even if they seem silly.
In data analysis and reporting, accepting figures at face value without asking basic questions can be dangerous. Using unemployment rates as an example, the text explains how a falling unemployment figure isn’t always a straightforward sign of economic improvement. Factors like people leaving the workforce, changes in benefits, or even mortality among the unemployed can influence the numbers, meaning a deeper look is necessary before drawing conclusions.
The article urges readers to challenge assumptions and ask those “silly” questions without fear, especially around data. Doing so reveals complexities that numbers alone can’t explain and prevents misinterpretation that can have real consequences. For anyone interested in understanding how data shapes our views, it’s worth reading the full article to see why curiosity matters more than ever.
Shopping can be frustrating when clothes don’t fit the way they should, especially for men who don’t match the ‘average’ sizes stores cater to. Labels like ‘big man’ or ‘one size fits all’ feel limiting and ignore real differences in body shapes. This reflects a bigger problem where marketing and media create perceptions that don’t reflect reality, leaving many people feeling excluded and overlooked.
The problem of labeling shows up just as much at work. Job titles such as ‘data scientist’ get thrown around without a clear definition, causing confusion and sometimes unnecessary competition between roles. Instead of relying on vague labels, it’s better to focus on what people actually know and can do. The skills and experiences everyone brings are unique, and one label can never fully describe a person’s value.
Moving away from rigid labels helps include more people and appreciate what they bring to the table. Whether in shopping or in careers, labels can box people in and deny their individuality. Understanding this encourages us to see people as more than just a name or category. If you want to explore this idea in more depth and consider how labeling affects us all, I encourage you to read the full article.
The idea of proving whether one pudding tastes better than another runs into some tricky problems. It’s not possible to prove that two things taste exactly the same because someone, somewhere, might disagree. You would need to test every single person to be sure, which is impossible. On the other hand, proving that one pudding is better can also be uncertain because samples might not represent the whole population or could be affected by chance.
This touches on how science works. It never offers absolute proof, only levels of confidence based on evidence. Even a large group of testers cannot guarantee a definite answer, only a likelihood. That uncertainty is part of why scientific thinking encourages skepticism and an openness to new evidence, rather than demanding final proof one way or another.
When it comes down to it, the question of which pudding is better is less about proof and more about weighing the evidence thoughtfully. It’s a reminder that many things we want to be certain about can only be approached with caution and curiosity. If you want to dive deeper into the challenges of proving “proof” itself, this article offers some interesting insights.
Ever had that weird neck twinge, rash, or headache that just won’t go away? You pop some ibuprofen, maybe some leftover meds, but nothing helps. So, naturally, you turn to Google. And suddenly, your harmless symptom looks like a rare, deadly disease. Panic sets in—you imagine the worst, maybe even say goodbye to loved ones, and rush to the doctor expecting the worst.
But then the doctor calmly tells you it’s just a mild case of sunburn. No crisis, no drama—just simple advice to drink water and take paracetamol. Relief floods in, along with the realization that you can’t replace years of medical training with a Google search.
Sounds familiar? Now imagine doing the same with data. Everyone thinks they can analyze data because they did math at school and have Google at their fingertips. But data isn’t just numbers; interpreting it correctly requires expertise, training, and experience—just like diagnosing health issues.
If you jump to conclusions without the right skills, it can lead to wasted time or worse—bad decisions affecting finances, patient care, or staffing. That’s why we need trained analysts, statisticians, and data scientists, not just casual dabblers.
If you’re involved with data in any way, make sure your training is solid. Check out my courses to stay sharp and be the trusted expert who gives the right “diagnosis” when it really matters.
Want the full story and more insights? Dive into the full article!
Ever wondered where your family name comes from or what it might mean beyond just a label? I’ve been fascinated by the story behind my surname, Varlow, and the challenges it brings, like getting it mixed up over the phone or even by Google. That curiosity led me to discover my family coat of arms, which includes the Latin motto Magna est Veritas, meaning Great is Truth. This phrase really resonates with me, especially in my work delivering training around data and research.
In the full article, I explain why truth isn’t just a nice-to-have but absolutely essential when working with data, whether you’re analyzing, interpreting, or making decisions. With so much data at our fingertips and powerful tools available, it’s tempting to focus on flashy visuals or quick results, but without honesty and rigor, that data can mislead or even cause harm. I share why Magna est Veritas is more than just a motto, it’s a reminder of our responsibility to defend the truth in everything we do with data.
If you’re someone who works with data, analysis, or research, or if you simply want to better understand how to handle information responsibly, I encourage you to read the full article. It’s packed with insights on why integrity matters, how to ask the right questions, and how to use data in a way that really empowers people. This is more than theory, it’s about real-world challenges and why truth must always come first. Give it a read and see why protecting the truth is at the heart of my training and my approach to data.
