1.1. Distinction Between Types of Risk

Now, the question comes, can we really measure risk, can risk be measured? (Refer Slide Time: 23:35) There is a person Lord Kelvin, he is saying that anything that exists; exists in some quantity and can, therefore, be measured, if there are 5 people, we can say 5 people so, it exists so, anything that exists, that exists in some quantity, a glass of water; yes we can tell it.

The section starts by questioning whether risk can be measured. The philosopher Lord Kelvin states that anything that exists can be quantified. This means that risks, which are often abstract, could theoretically be quantified if they exist as real entities. For instance, if there are 5 accidents reported, this is a measurable quantity of risk associated with that area.

Think of measuring the weight of a package. Just as you can say a box weighs 10 kilograms objectively, you can measure risk (like the risk of accidents on a road) based on statistical data.

So, a distinction should be made between real, actual, objective measurable risk, which follow the scientific rules, scientific law of statistics. So, statistically we can say how much risk is there, another one is the subjective one that perceived by non-expert, there is nothing called cultural risk, it is your problem man, but there is a scientific.

The text emphasizes the importance of distinguishing between objective and subjective risks. Objective risks can be measured scientifically through statistics—these follow rigorous analytical rules. Conversely, subjective risks are interpreted based on personal opinions or perceptions, often lacking scientific backing. The author suggests there's no such thing as cultural risk; it's merely a personal perspective.

Consider how people react to the risk of flying. Statistically, flying is safer than driving. While one person may feel anxious about flying based on personal experience, another may feel secure based on hard data showing it's safer. Their differing feelings illustrate subjective versus objective risk.

Now, Britain Department of Transport, they are saying that yes we can measure the risk, it is very simple, we can measure it based on casualty record, how many people are dying in a particular time and a particular road. The consequence of real accidents, these are simple parameters to tell people how much risk is there.

The excerpt highlights that the British Department of Transport claims measuring risk can be straightforward. They suggest using casualty records—tracking fatalities over specific timeframes on particular roads—as a method of quantifying risk. This data-driven approach allows for an objective assessment of road safety and related risks.

Imagine a grocery store that tracks how often customers slip and fall. If they keep a record of these incidents over a month, it becomes easier to see which areas are riskier and need better signage or mats to prevent accidents. This is akin to measuring road safety through accident records.

Also, this is actual danger because if you want to be flamboyant, you can be, no problem but that is the consequence. And if you do it, if you do not believe it, this is your personal problem but, scientifically we know that this road is danger, you should not do this so, you are at risk.

The text discusses the clear distinction between safe and unsafe roads based on accident data. It suggests that regardless of personal beliefs about a road's safety, scientific data can dictate what is considered dangerous. If a road has a high accident rate, it is deemed unsafe, which is a fact supported by statistics.

Think about wearing a helmet while biking. Some might feel helmets are unnecessary and choose not to wear one. However, statistics showing that helmet use significantly reduces injury rates illustrate that, regardless of personal belief, data supports safer behavior.

So, really; can we really measure the risk? so that is a big question, these questions that whether we can really measure, can we really distinguish between objective risk and perceived risk that challenge will continue. In our next series, we will discuss on this aspect that how, when, what extent we can distinguish between objective risk and subjective risk and can we really do it, so and what and how extent it will affect the preparedness.

This part concludes the discussion by reiterating the ongoing challenge of measuring risk accurately. It raises important questions about the ability to differentiate between objective risks—those backed by data and statistics—and perceived risks—those based on individual belief or sentiment. The text suggests that future discussions will delve deeper into these distinctions and their implications for risk preparedness.

Imagine preparing for a storm. Meteorologists use data to predict the likelihood of severe weather (objective risk), while individuals might feel nervous about storms based on past experiences (subjective risk). Acknowledging both perspectives helps in making better preparedness plans.

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