1.3. Types of Risk

Here is a very important data, then what is risk? Why people are not believing risk? there is a Britain Royal Society; they publish a White book on risk assessment in 1982 and in 1983, it was revised again.

Experts and scientists are called and but disagreement continued about risk.

This chunk introduces the concept of risk, questioning what it is and why some people may have skepticism towards it. It references the Britain Royal Society's efforts to assess risks through a published report in 1982, demonstrating that even with expert opinions, there remains a disagreement about understanding risk.

Think of how people react differently to health warnings. For example, when authorities warn about the dangers of smoking, some people still choose to smoke, despite the risk. This reflects a skepticism or disagreement with the presented risks, much like the response to the Royal Society's report.

So, actual risk we as scientists saying that we there is actually an actual risk, what is that? So, we are saying that there are 2 kind of risk; one is objective risk that is scientific risk; another one is the perceived risk.

The distinction between objective risk and perceived risk is introduced. Objective risk refers to risks that are measurable and based on scientific data, while perceived risk is based on individual beliefs and interpretations of a situation. Understanding this difference is crucial in risk assessment and management.

Consider a businessman deciding whether to invest in a new technology. The objective risk involves market data and statistical analyses showing potential returns. The perceived risk, however, might include his fear of changing technologies or remembering past failures in similar ventures, illustrating how personal beliefs affect decision-making.

Now, risk in general, we know the probability of a particular adverse event to occur during a particular period of time. So there is a probability question in a particular time question.

This chunk explains risk as a probability of an unfavorable event occurring within a specific timeframe. It emphasizes that risks must be quantified over a certain period, adding a time dimension to the concept of risk. This foundational understanding is key in both scientific and everyday contexts.

For example, when we hear weather forecasts predicting a 70% chance of rain tomorrow, they are conveying the risk of rain based on historical patterns, which is a form of assessing risk where time is an integral factor.

So, determinant of risk; how we determine a risk? Generally, we determine any kind of risk by numerical measures, like expressed in chances of that much cost in dollar or in rupees, loss is expected to due to a flood.

This chunk discusses how risks are often determined and expressed in numerical terms, such as financial costs or human casualties. Numerical measures make risks tangible and understandable, allowing for clearer assessments and decisions regarding risk management.

Consider an insurance company assessing risk for flood coverage. They might calculate the likelihood of flooding in a region and estimate potential losses in financial terms, say $10 million, helping them set appropriate insurance premiums.

Now, risk estimation progress; if we want to progress more, if you want to refine our estimation, one thing is very clear that we need data, without data we cannot do it.

The importance of data in refining risk assessments is highlighted here. Reliable, comprehensive data enhances the accuracy of estimations, indicating that data-driven approaches are essential for effective risk management and decision-making.

Imagine a scientist trying to understand the risks of a new drug. They would gather extensive clinical trial data to evaluate potential side effects accurately. Without this data, their assessments would be based on guesswork, leading to potentially harmful consequences.

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