Lesson 2: Reading Lesson

Assessing Risks to Society

Everyday Use of Probability

Percentages and probabilities are related, but not the same. Percentages are a mathematical statement of how many times out of 100 something happens. Probabilities refer to just one occurrence. For example, a 30 percent chance of rain at a particular weather station means that given these same weather conditions for 100 different days, it is expected to rain 30 of those days. The probability of rain for any one of those days is 30 divided by 100, which equals 30/ 100 or 0.30.

30 / 100 = 0.30

Repeated Observations and Experiments

Most of the probabilities we use in every day life are determined from simply observing what happens every time certain conditions arise or from repeating an experiment many times. The number of times that a specific outcome occurs, divided by the total number of times the experiment is repeated, is the probability that the specific outcome will occur. This is useful in making predictions about what will happen in the future.

Common Sense

Some probabilities are common sense. For example, we know that when we flip a coin, there are only two possible outcomes — heads or tails. So there is a 50 percent chance (a 0.50 probability) that the coin will land heads up. There is also a 50 percent (0.50 probability) chance that it will land tails up. If we want to know how many times a coin is expected to land heads up on a certain number of flips, we don’t have to actually flip the coin. We can simply multiply the probability of heads by the number of times we would flip the coin.

Random events like the coin flip cannot be predicted with certainty. Every time the coin is flipped, there is a 0.50 probability of heads and a 0.50 probability of tails. If a lot of flips in a row land heads up, the probability that the next flip will be tails is still 0.50.

Figuring Probability

The same principles apply to other events. Suppose there are a certain number of possible outcomes from an event, and each event has an equal chance of happening. Then the probability of each outcome is one divided by the number of possible outcomes.

For example, the probability of drawing the ace of spades from an ordinary deck of cards is 1/52 — because there is only one ace of spades in a 52-card deck. Now, if we want to know the probability of drawing any ace on one draw, we add together the probabilities of getting each particular ace. There are four aces out of the 52 cards, or one ace per suit. Thus, the probability of drawing any of the four aces on a single draw is 1/13.

Probabilities are usually not presented in fractions but are expressed as a number between zero and one. The example above is repeated to illustrate this. The probability of drawing the ace of spades from an ordinary deck of cards:

This means there are four favorable outcomes out of 52 possible outcomes:

Suppose you are playing cards and you want to know the probability of drawing a particular hand — five cards of the same suit (a flush). Think of each draw as a separate event. Remember also that the number of favorable and possible outcomes will be reduced by one after each draw.

First draw: There are 13 favorable cards out of 52 total cards; 13/52

Second draw: There are now only 12 favorable draws out of 51 cards; 12/51

Third draw: There are 11 favorable cards remaining out of 50 cards; 11/50

Fourth draw: There are 10 favorable cards remaining out of 49 cards; 10/49

Fifth draw: There are nine favorable cards remaining out of 48 cards; 9/48

To determine the probability of independent events happening together, you must multiply the individual probabilities together.

So for drawing a flush:

The chance of drawing a flush in any suit in five draws is 198 in 100,000. If you drew five cards 100,000 times, you would be likely to draw a flush 198 times.

Health and Safety Risks

Other probabilities, including those for health and safety risks to humans, are harder to determine. A lot of information may be needed to make a prediction. Or testing the whole system for which risks are being evaluated may not be possible. However, once the basic probability for each possible outcome is known, the same rules apply and can be used to make reasonable predictions.

For instance, suppose that, by law, a company cannot distribute a machine until certain safety standards are met. The company knows the machine will not operate safely if two particular parts break down at the same time. This situation could exist if one part is a backup for the other. The company couldn’t wait until after the machines were distributed to see how many times out of 100 the two parts would break down at the same time.

However, the company could conduct tests on each part to find the probability for each part breaking down. Then these probabilities could be multiplied to determine the probability of both parts failing at the same time.

For example, suppose tests determined that the probability of part A breaking down was 0.05 and the probability of part B breaking down was 0.02. Thus, the probability of both parts breaking down is 0.05 x 0.02. This equals 0.001 or 1/1,000 (one in a thousand). If that level of risk is acceptable to the company and meets industry regulations, then the company could distribute the machine.

ALARA

Nuclear waste disposal is governed by a principle of minimizing risk to the public and environment to “as low as is reasonably achievable” — ALARA. Many steps are taken in nuclear waste disposal, as with other nuclear power activities, to reduce public risk from radiation to below acceptable risk levels determined by regulatory authorities (10-2 to 10-6 per year is the typical range of acceptable risk).

Limitations

One problem is that to know if risk increases, we have to know what the pre-existing risk is before the “new risk” is introduced. Often, increased risk is based on laboratory experiments using large numbers of animals. Large numbers of subjects are helpful, but because there are significant biological differences between the test population (often rats or mice) and humans, many uncertainties are introduced.

Probabilities do give us a way to determine a level of risk that is more or less objective. But it is important to understand that personal judgment is still involved. For example, choosing what to consider in an experiment requires some judgment.

Consequences and Values

Determining the acceptability of risk involves both the consequence of the action in question and personal values. If you decide not to carry an umbrella, the consequence may be that you get wet if it rains. How much risk you are willing to accept depends on whether you mind getting wet..

Of course, in many situations, decisions are much more complicated than whether to carry an umbrella. It is especially difficult to determine how much risk is acceptable when the consequences of an event, action, or technology could involve some risk to human health or safety.

For example, say a prescription drug that can help alleviate severe pain, may also increase a person’s risk of having a heart attack. Before taking the drug, it helps to have some way of quantifying that risk. Is there a .05 percent chance of having a heart attack or a 50 percent chance? What other factors increase the risk (such as family history, age, drinking alcohol, smoking, etc.)? Ultimately, the decision on whether or not to use the drug depends on someone’s judgement whether the benefit of the drug is worth the risks involved.

A value judgment is always required to determine the level of risk considered acceptable.

Making Societal Decisions

Using probability as a tool for discussing risk is useful, but it is important to recognize that there are limitations in using probability for making decisions about the acceptability of risk. For example, most societal issues in which risk is a factor are complex. A significant problem may be discounted or underestimated. Also, many probabilities are only estimated because it is not possible to perform controlled experiments to measure them. Furthermore, human behavior and human error are even less predictable than physical or biological events.

Other Aspects of Risk

Probability is only one aspect of risk. Societal risk decisions also involve consequences and values. What is the consequence of a failure — loss of money, illness, death? How large are the consequences? Do the risks and benefits fall on different people? Do the risks fall on the decision-makers or on others? How are decisions made? What are the alternatives?

Risk Perception

When making decisions that affect society, we must also factor in people’s perception of risk, which, in some cases, may be different from the quantified risk. For example, in many surveys, people have indicated that they feel safer traveling by car than on an airplane, yet mathematical assessments show the quantified risk to be just the opposite. According to the National Safety Council * the lifetime odds for a person born in 2001 of dying in a car accident in the United States is 1 in 247, while their lifetime odds of dying in an airplane crash is 1 in 4,023.

*National Safety Council estimates based on data from National Center for Health Statistics and U.S. Census Bureau. Deaths are classified on the basis of the Tenth Revision of the World Health Organization’s “The International Classification of Diseases” (ICD). Numbers following titles refer to External Cause of Morbidity and Mortality classifications in ICD-10. One-year odds are approximated by dividing the 2001 population (285,093,813) by the number of deaths. Lifetime odds are approximated by dividing the one-year odds by the life expectancy of a person born in 2001 (77.2 years). http:// www.nsc.org/lrs/statinfo/odds.htm

Taken another way, according to the above statistics, people are over 16 times more likely to die in a car accident than in a plane crash in their lifetime. However, many people feel safer in a car because a car is more familiar; because it is not as technologically advanced as an airplane; because they have more control over a car; and because a car crash somehow doesn’t seem as catastrophic as a plane crash. On the other hand, most pilots feel safe flying because they fly all the time, making it more familiar to them; because they have a better understanding of the technology; and because they themselves are in control of the plane.

People tend to evaluate risk with the following questions:

• Is the risk familiar, or is it out of the ordinary?
  
  
• Is the risk complicated, or is it understandable?
  
  
• Am I in control of the risk, or is someone else?
  
  
• Is the risk my choice, or is someone forcing it on me?
  
  
• Does the risk benefit me in some way, or is the risk for nothing?
  
  
• Could the risk result in hurting a lot of people very badly (catastrophic), or would just a few be hurt and in a minor way?

The more a risk seems unfamiliar, complicated, out of control, forced, detrimental, and catastrophic, the more unlikely we are to assume the risk regardless of the mathematical odds.

In solving the problems associated with nuclear waste, we must make decisions based on the quantified risks to people and the environment. However, in a democratic society we must also deal with people’s perceived risks. To most people, any decision concerning nuclear waste will seem very risky because of the above factors.

To deal with the perceived risks, we must do more than just quote statistics. We must work to reduce and minimize the factors that lead to people’s fear and mistrust.

In the next lesson, you will learn about the methods required by the Nuclear Waste Policy Act that are intended to reduce both the quantified and perceived risks associated with nuclear waste.