How Can You Predict Tomorrow?

Abraham Lincoln once said: “The best way to predict the future is to create it.” 

Will you wake up rested? Will your train be late? Will you finally take that walk, call your mother, close the deal? For centuries, the future was the province of prophets and crystal balls. However the biggest discoveries were made with numbers, not fortune tellers. The future is transcribed into numbers. 

PS Writes an author, who miraculously scored 56% in Maths in the Matura exam and truly detested Statistics classes at the University of Rome. But it’s not about me. 

Statisticians don’t claim clairvoyance. What they offer is something more grounded and arguably more powerful. Not a vision of what will happen, but a careful, data-driven forecast of what probably will. In an era defined by uncertainty - geopolitical shocks, climate swings, erratic economies, this quiet discipline has become an indispensable lens through which we understand the shape of the days ahead.

The Magic Behind the Mundane

At first glance, statistics might feel too sterile to carry the poetic weight of tomorrow. But ask Nate Silver, or the analysts at FiveThirtyEight, or your local weather forecaster: the ability to analyze past data and project forward - through regression models, confidence intervals, and probability distributions - can turn chaos into pattern.

Take your morning commute. Traffic congestion isn’t random; it’s seasonal, cyclical, and measurable. Millions of data points - from GPS trackers, road sensors, even weather data - feed into algorithms that now power your favorite map apps. The “you’ll arrive in 37 minutes” prediction isn’t magic. It’s math, modeling what similar Tuesdays have looked like before.

Statisticians call this Bayesian inference: updating beliefs in light of new evidence. If it rained yesterday and traffic jammed, and rain is forecast again, you adjust your odds. Every one of us, often unconsciously, becomes a living, breathing probability calculator.

Your Daily Decisions Already Depend on It

Statistics already shape your choices - from health to finance to relationships.

That smartwatch nudging you to stand up? It’s part of a machine learning system tracking your behavior, predicting risks (sedentary lifestyle, irregular sleep), and suggesting micro-adjustments. Investment firms use Monte Carlo simulations to predict how your retirement fund might fare under thousands of future economic scenarios. Online dating apps? They don’t just match you - they estimate, based on past user behavior, who you're most likely to message, like, and eventually meet.

In other words, the architecture of your daily life is increasingly statistical. The question isn’t whether statistics can predict your tomorrow - it’s whether you’re paying attention. 

Would you like to know the date of your death?

Most people answer NO, I don’t-death-wish. But we know how many and where people die… in Statistics. We know when people tend to die - age, season, even day of the week. We know how - diseases, accidents, violence, and increasingly, lifestyle-related conditions. Statistics collect these fragments of fate and arrange them into patterns, cold and indifferent. But hidden in those numbers is something more than just morbidity. There’s a mirror, and perhaps a map.

Take, for instance, actuarial tables used by life insurance companies. They don't tell you your death date, but they can tell you how long someone like you is expected to live. Age, gender, smoking habits, health status, occupation - each factor adds weight to the scale. It's not prophecy, but it is probability.

And so, when we ask, “Would you like to know the date of your death?”, what we're really asking is: How much of your life are you willing to look at through the lens of data? Would you face your fate if it came in percentages and charts instead of omens and shadows?

The question is no longer “Can we know?” but “Do we want to?”.

When the Numbers Fail — And Why That Matters

But let’s be honest: prediction is never certainty. Even the best models have blind spots. Polls are the best example however poisoned with ideology, bias and political agenda. That’s the biggest threat for the science: bias and populism, therefore lack of fact based data. Polls missed a major win of Trump in 2016. Economists missed the 2008 crash. Pandemic models, at times, wildly diverged. Certainly it creates a fertile ground for conspiracy theories, but the answer is the numbers don’t lie, people do. 

Why? Because humans aren’t entirely predictable. Because data is often messy, incomplete, biased. And because statistical models are only as good as the assumptions that power them.

Which is why the best use of statistics isn’t prediction alone - it’s preparation. Good models tell you not just what might happen, but what to plan for. They define your risk landscape, your confidence bounds. They don't say, “Tomorrow will rain,” but “There’s a 70% chance.” And that nuance — that intellectual humility — might be the most powerful insight of all.

We live in routine

In a world saturated with information, the ability to distill signal from noise is no longer optional. Statistics, when used well, provide a toolkit for seeing the world not as fixed, but fluid; not binary, but probabilistic. And when something is more probable, you can plan around much easier. 

“The future” isn’t a singular event waiting to arrive - it’s a set of unfolding possibilities, shaped by countless tiny decisions, all measurable, all meaningful.

So tomorrow, when your weather app says 30% chance of rain, don’t scoff. Smile. It’s not a shrug - it’s a window. A glimpse, through the lens of mathematics, into the shimmering maybe of what comes next.

And that’s not just useful.

It’s profound. 

Chaos is a beautiful… whole

Chaos creates chaos. People create structures. Relationships, borders, governments, business ventures… The goal is simple: to live a fulfilling life. By the plan.

If probability is our compass, then statistics is the map. And with it, we may not control the future - but we can certainly learn to navigate it.