# Physics 3333 / CFB 3333 Risk and Probability

**Click here for Professor Fisher's slides on probabilities and probabilistic fallacies. (password required)**

**Click here for Professor Fisher's slides on using probabilities in decision making. (password required)**

# Evaluating Risks and Probability

## What Is Risk Anyway?

Risk is the probability that something undesirable will happen. Whatever it is, you won't like it. You wouldn't call the possibility of winning a million dollars a risk - you'd want that to happen.

The reason for evaluating risk is that practically everything is uncertain. Maybe it'll happen - maybe it won't. How to evaluate the probability?

## A Quick Look at Probability

Probability is simply a way of describing how likely it is that some random
event will occur. A probability is normally expressed as a number ranging
from 0 to 1. A probability of 0 means that the event **cannot** happen;
there is some law of physics (or something else) that prevents it absolutely.
A probability of 1 means that the event will **always** occur. A number
greater than 0 and less than 1 expresses how likely the event is. A probability
of 0.01 (1%) means that the event is expected in about 1 out of 100 times.
A probability of 0.5 (50%) means that the event is as likely to occur about
half the time; when flipping a coin heads should occur in about half the throws.
A probability of 0.9 (90%) means that the event will happen 9 out of 10 times.

## The Basis for a Probability Statement

If you see a statement of a probability for something, it might be useful to know the basis for the number given. There are three possibilities.

- Degree of Belief
- Propensity
- Frequency

**Degree of Belief:** This applies to single events where there is no
base of experience to draw on. Consider some statements (which we made up).

- This new surgical procedure has an 80% chance of success.
- My chance of winning the Cliburn piano competition is 60%.
- I bought a stock my brother-in-law recommended. There's a 90% chance it's going to really take off.

**not**occur. If the surgery fails, that was the 20% chance of failure.

Now for an important principle - belief is **not** certainty **unless**
it is supported by evidence.

**Propensity:** This is an analytical value based on knowledge of the
mechanisms involved in the events. For example, take an ordinary six-sided die.
If you know that is is well-made, with its center of mass precisely at the
geometric center of the die and its shape is a perfect cube, then any given
face of the die should land on top with a probability of 1/6 (0.166666...).
You can say this **before** you ever throw the die to test it.

**Frequency:** Here we're talking about actual frequency of occurrence.
This is experience, or data. Let's consider a die again. Suppose you have
a die and you **don't** know how it is made. There's only one way to
evaluate the die - throw it several hundred times and record what happens.
If, in fact, each of the faces appears about 1/6 of the time, you would conclude
that the die is well-made and fair. On the other hand, if the die displays
a shortage of sixes and a surplus of ones, it's probably loaded on the 6 side.
You determine the probabilities by actually testing it.

## Why Look at Probability Anyway?

We look at probabilities because practically everything is uncertain to some extent.
Outcomes cannot be predicted exactly. There is one thing, however, that is
reasonably certain (OK - highly probable), and that is that you don't like uncertainty.
It is very hard to deal with uncertainty, particularly when risk is involved.
You want positive answers. The only problem is that, in many cases, positive,
certain answers are not available. You **must** deal with uncertainty,
and probability is a tool for doing that.

## Some Nitty-Gritty About Probability

We're now going to go into a bit of detail about probability. Follow along carefully.
The first thing is a little notation - a way to write probabilities. We use

p(A) = 0.6

to indicate that the probability of event A is 0.6. The probability of A
is written as p(A). The probability of B would be p(B). You see the idea.
Let's make up an example. Suppose you select any random SMU student.
What is the probability that the student is interested in going to Mustang
football games? You might guess a probability by finding out how many students
actually go to the games and dividing by the total number of students at SMU.
That would give you an estimate of the probability. You could write it as

p(interested in football) = 0.3 (we made up the number)

Let's keep going here. Suppose you select the random student and discover
that said student has a ticket for the next football game. Now what is the
probability that your random student is interested in the football games?
It's a LOT higher! You would write the probability like this:

p(interested in football | has game ticket) = 0.8 (made up the number)

This represents the probability that our random student is interested in
going to Mustang football games **given that** the student already has a
ticket for the next game. This is what's called conditional probability.

p(A|B) = 0.4

This represents the probability of A given that B is true **and** that
B has some effect on A. If B has absolutely no effect on A, then you will have

p(A|B) = p(A).

A and B are independent. Now - back to football games. Suppose that your
random student mentions that they are taking a journalism class. What is

p(interested in football|taking journalism)?

If there is no connection (likely), then

p(interested in football|taking journalism) = p(interested in football).

Knowing that they are taking journalism doesn't give you any information
about their interest in football.

## Risk: Warranties vs. Insurance

**Example: Extended Warranties.**
You buy a $1000 television, which comes with a 1-year warranty. The store offers to extend this warranty to 4 years for only $200 more. Now, on the surface, this can seem like a small additional price to pay for the peace of mind that your expensive television is protected for 4 years. But thinking critically, we realize that the store would only offer this deal if it were profitable (indeed, many stores push these warranties very aggressively, suggesting that they are, in fact, quite profitable). A little math suggests that the store would only lose money on this deal if 20% of all TVs failed within 4 years -- if that were true, the manufacturer would surely go out of business! Therefore, an extended warranty is usually a very bad deal, *unless* you have specific reason to suspect that the failure rate of a product is unusually high.

**Example: Insurance**
The exception to this rule is when the potential loss, however unlikely, is so catastropic that it would drive you into serious financial problems. Examples, in decreasing order of loss potential, include your life, health, home, and automobile. Rather than *warranties* on these quantities, there exist mechanisms to protect against their loss, which we call insurance. Insurance companies maintain extensive research on how much they will *likely* have to pay to various kinds of clients (called *actuarial tables*), and charge you a slight premium over this amount to remain profitable.
The difference between being scammed and being prudent is largely one of relative scale: if you can afford to replace something yourself, you should not pay extra to insure (or warranty) it, but if you *can't* afford to replace it, you should consider it. For instance, *you* might maintain comprehensive (as opposed to just liability) auto insurance on a new car, because it represents a significant fraction of your net worth. However, a bus company would not maintain such insurance on its fleet vehichles -- it would be cheaper for it to hire an in-house mechanic to fix them as they break down. From this perspective, so-called "high-deductable" insurance, which is significantly cheaper than "low-deductible" insurance, but kicks in at a higher level, is usually desirable assuming you have a cushion funds to make up the difference in deductible (which is wise).

## Products that protect against rare occurrences

A topic closely related to loss insurance is the purchase of products designed to protect you from undesirable circumstances. Examples include home and auto alarms, and "identity protection" services such as LifeLock, which are often quite expensive. Certainly, any product that reduces risk has some value, and you might imagine that the value is propotional to the degree by which risk is reduced. As a concrete example, consider each of the following technologies designed to reduce automotive accident fatalities:

- Lap Belts
- Shoulder Belts
- Front Airbags
- Surround Airbags
- Disc-braking systems
- Anti-lock disc brakes
- Automatic Braking Systems
- Fully Computer-Driven Cars

(Political Aside: Compared to how much the auto industry has done to reduce auto-related deaths, how much has the gun industry done to reduce gun-related deaths?)

## Survivorship Bias

Suppose you receive a letter on fine stationary, informing you that you have been selected to participate in a "free trial" of a new stock market analysis tool. The letter states that based on sophisticated analysis, the tool predicts that stock market is expected to go up, and so you should be *in* the market that week. Indeed, the market goes up. The next week, you receive a second letter, this time predicting the market will go down, and so you should "short" the market. Indeed, the market goes down. This continues for *10 weeks* in a row, after which you are informed that your free trial has ended, and further participation requires a significant subscription fee. Do you participate? The appeal of this scam comes from a faulty assumption about the sample size. Assuming the market goes up or down with equal likelihood, you should calculate that the chances of correctly predicting the outcome 10 weeks in a row is only 1/1024. You might think it is exceedingly unlikely that the letter sender could do this by chance. However, this is only true if you are the only recipient. If the sender has thousands of other targets, your "amazing" predictions become predictable. Here is how it works: each week a large sample is randomly divided into two halves. One half is sent a prediction that the market will go up, while the other receives a prediction that it will go down. After the week, the addresses to which incorrect "predictions" were sent are *discarded* from the sample, and the process will be repeated with the remaining addresses. A small number of recipients will have received a string of predictions that seems truly amazing, but only because the failed predictions were discarded. The final sample is baised heavily in favor of the survivors. This effect is actually ubiquitous throughout the financial industry. For example, suppose your financial advisor recommends giving all your money to the celebrated hedge fund manager, "Albert Goldfinger." Mr. Goldfinger has become famous for beating the market in 14 of the past 16 years, an enviable record to be sure. However, let us think more critically about whether we should be impressed. We can *beat* the market about 50% of the time by random chance, simply by overweighting or underweighting stocks. Doing this 14/16 times would occur with probability 1 / 2**16 *(16*15) = 1 / 273 There are many more than 273 hedge fund managers, and so we would expect that one person *always* has such a record! The same situation applies to the well-repsected mutual fund. Suppose a survey of all "actively-managed" mutual funds finds that the funds have returned, on average, 0.1% more than "index funds" over the past 5 years, despite the added costs incurred by management fees. You might think favorably about such funds -- i.e., that the management they are providing is worth the associated fees. However, this neglects an important fact -- a significant fraction of all mutual funds close down each year. A more active statistic would be to survey the performance of all funds *that existed 5 years ago* over the past 5 years. Because the worst-performing funds have closed down, they are removed from the sample of currently-existing funds, artificially inflating the performance of that sample. http://www.scientificamerican.com/article/math-explains-likely-long-shots-miracles-and-winning-the-lottery/## Applications in Health Care and Medicine

One area where you will see risk mentioned a LOT is in health care and medicine. These risks will be described with some form of probability. You'll see such accompanying reports of diseases, treatments and tests. You might think that such probabilities would be straightforward and understandable, but this is not the case. There are mathematically defensible ways to represent those probabilities that will mislead you if you don't know how to read them.

## An Example

An example please, Professor. OK - here's one. There is a relatively serious disease called crudulosis (don't ask your doctor about this). There is a vaccine that helps, although it is not perfect; it reduces the risk of getting crudulosis by 50%. Sounds good, eh? But what does it really mean?

Let's look at the real statistics for crudulosis. Not everybody gets it; it
occurs in about 1% of the general population (about 1 in 100). That means
that the risk of catching it is 1% (guess most of us can resist it).
The vaccine reduces the risk of getting crudulosis to 0.5%. This is an
**absolute risk** reduction of 0.5% (from 1% to 0.5%). Now, a reduction
of 0.5% certainly won't grab anyone's attention, so we try another tack.
We divide the risk after vaccine by the risk before and get 0.5/1, which
is 0.5, or 50%! The vaccine reduces the risk by half! Now that is an
attention getter! The 50% number is a **relative risk**. If you read
about some risk reduction expressed as a relative risk (and that's how
most will be expressed), remember that you really don't know what the
**actual** risks are unless you are given the **absolute risk** numbers.

There's one more number you likely will NOT see reported. It goes like this.
Given that the vaccine sort of works, how many people must you vaccinate in
order to prevent **one** case of crudulosis? We'll simply divide the
**absolute** risk reduction into 1; we'll get 200 in this case.
We must vaccinate 200 people to prevent one case of crudulosis. This number,
200 in this case, is known as the **Number Needed to Treat**.
It would be very unusual if you saw this number.

Does looking at it a different way change your evaluation of the vaccine?

## Another Example

There's good news on the crudulosis front: someone has developed a test which can help determine if you are in the 1% likely to get the disease. The test isn't perfect (no test is), but it is useful. Here are the statistics for the test.

- p(positive|patient prone to crudulosis) = .995 (true positive)
- p(negative|patient prone to crudulosis) = .005 (false negative)
- p(positive|patient NOT prone to crudulosis) = .04 (false positive)
- p(negative|patient NOT prone to crudulosis) = .96 (true negative)
- base rate = 0.01 (1%) base rate of crudulosis

**true positive**probability is very close to 1, which means that the test will, for all practical purposes, correctly identify anyone prone to getting crudulosis. The

**false positive**probability is 0.04 (4%), which means that 4% of the time the test will come up positive on someone who is NOT prone to crudulosis. Remember this; the false positive rate is VERY important, and it is one number you will NOT likely see reported or correctly interpreted.

Now for the exercise. Suppose you read these numbers in the newspaper and
decide to go for testing to see if you ought to get crudulosis vaccine.
Sure enough, your test is positive. What is the probability that you
**actually** should go get vaccinated? Write down your estimate.

Not so easy, is it? If you find it baffling, don't feel bad. Most doctors don't know how to do it either.

## Representing This So You Can Understand It

The formal way of evaluating this is called Bayes' Theorem.
To show this we have to abbreviate a bit.

(base)*p(positive|prone) p(prone|positive) = ---------------------------------------------------------- (base)*p(positive|prone) + (1-base)*p(positive|not prone)

Ferocious, isn't it? Very few people will figure out how to evaluate this. There is hope, however. If you can learn to convert the statistics from probabilities to

**natural frequencies**, they will make sense.

To do this, let's assume that 100 people are screened with the new test. What's going to happen? Look at the data for the crudulosis test above.

- Crudulosis is found in about 1% of the population.
- The test is essentially certain to detect someone who needs to be vaccinated.
- 4% of people who do NOT need vaccination will test positive anyway.

100 (sample to test) / \ / \ Positive 5 95 Negative / \ \ / \ \ 1 4 95 (true (false (true positive) positive) negative)

In testing 100 people, we get 5 positives. Now - given a positive test, what is the probability that the individual needs to be vaccinated? It's 1 in 5, or 20% (0.2). Surprised? What this (imperfect) test has done is allow the probability that an individual testing positive actually needs vaccination to be increased from 1% (base rate) to 20%. It does

**not**indicate need for vaccination with certainty.

Once you convert the probabilities to natural frequencies, the whole thing is a lot clearer. You can also see that the false positive rate is very important. As that rate gets higher, the value of the test for screening decreases; you'll spend a lot of time and resources checking out false positives.

## The Illusion of Certainty

We said earlier that most people don't like uncertainty. They want solid, positive answers, not probabilities. Unfortunately, life isn't always certain. That said, we need to note that there are times when certainty is claimed when, in fact, it does not exist. Sometimes DNA matching and HIV tests are claimed to be "absolutely certain" and always correct. This is not true. All of these tests have small false positive rates. There is an illusion of certainty which is not justified. Also - any premise based solely on belief and without evidence is NOT certain.

One more statistic you need to be aware of is the Number Needed to Treat (NNT).
After you have defined a "bad outcome," the NNT indicates how many people
you must treat/vaccinate/etc to prevent **one** bad outcome.
NNT is derived from the reduction in **absolute risk**.

NNT = 1/(absolute risk reduction)

In our crudulosis example above, the reduction in absolute risk achieved
by the vaccine is from 1% (.01) to 0.5% (.005). The reduction in absolute
risk is .01-.005, 0r .005. THe NNT is then 1/.005, which is 200.
This means that you must vaccinate 200 people to prevent 1 case of crudulois.

Outline