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Statistics and Probability

Statistics and Probability (Part 1): Thinking in Uncertainty

Why certainty is rare, the difference between prediction and truth, and what probability really means.

Statistics· 2 min read

Intro: Thinking in Uncertainty

Probability is not just a mathematical subject, but a way of thinking about the world. Many real-life situations involve uncertainty, incomplete information and changing conditions. Instead of offering certainty, probability helps us reason about what is likely to happen.

  • Used when outcomes cannot be guaranteed
  • Helps guide decisions under uncertainty
  • Focuses on likelihood rather than certainty

Why Uncertainty is Fundamental

Experts making different predictions from the same evidence

In real life, certainty is rare, because most situations involve incomplete information and many interacting factors.

Even experts must make decisions and assumptions without knowing all the variables.

Consequently, multiple predictions can be equally justifiable at the same time, even when they are built on the same evidence.

Prediction vs Truth

Prediction compared with truth

A prediction is an estimate about what is likely to happen, while truth is what actually occurs.

Predictions are not meant to guarantee outcomes, as they are not always accurate, but they are intended to guide decision-making under a level of uncertainty.

Probability helps us measure how "confident" we should be in a prediction.

What Does Probability Really Mean?

Randomness and missing information as sources of uncertainty

Probability can represent:

  • Long-term frequency, such as repeated patterns (like coin flips)
  • Confidence in unique guesses (like human behaviour)

Uncertainty comes from either natural randomness or simply missing information.

Probability is the tool we use to manage both and make a choice.

Real-Life Examples of Uncertainty

Uncertainty appears in weather forecasts, sports outcomes, medical risk assessments and exam revision strategies.

In each case, outcomes cannot be known in advance, but some results are more likely than others.

Probability allows us to compare these likelihoods rationally.

Examples:

  • Weather: forecasts use "ensemble modelling" to calculate likelihood, such as a 60% chance of rain.
  • Medical: risk assessments use data and assumptions to prioritise safety.
  • Sports and strategy: success depends on managing risks rather than avoiding them entirely.
  • Education: revision strategies often rely on identifying "likely" topics while acknowledging the unknown.

Part 1 Takeaway

Instead of thinking "This will happen", we begin to ask "How likely is this to happen, and why?"

Probability shifts thinking from false certainty to informed judgement.

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