WEBVTT
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You've tested positive for a rare and deadly
cancer that afflicts 1 out of 1000 people,
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based on a test that is 99% accurate. What
are the chances that you actually have the
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cancer? By the end of this video, you'll be
able to answer this question!
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This video is part of the Probability and
Statistics video series. Many natural and
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social phenomena are probabilistic in nature.
Engineers, scientists, and policymakers often
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use probability to model and predict system
behavior.
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Hi, my name is Sam Watson, and I'm a graduate
student in mathematics at MIT.
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Before watching this video, you should be
familiar with basic probability vocabulary
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and the definition of conditional probability.
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After watching this video, you'll be able
to: Calculate the conditional probability
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of a given event using tables and trees; and
Understand how conditional probability can
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be used to interpret medical diagnoses.
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Suppose that in front of you are two bowls,
labeled A and B. Each bowl contains five marbles.
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Bowl A has 1 blue and 4 yellow marbles. Bowl
B has 3 blue and 2 yellow marbles.
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Now choose a bowl at random and draw a marble
uniformly at random from it. Based on your
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existing knowledge of probability, how likely
is it that you pick a blue marble? How about
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a yellow marble?
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Out of
the 10 marbles you could choose from, 4 are
blue. So the probability of choosing a blue
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marble is 4 out of 10.
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There are 6 yellow marbles out of 10 total,
so the probability of choosing yellow is 6
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out of 10.
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When the number of possible outcomes is finite,
and all events are equally likely, the probability
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of one event happening is the number of favorable
outcomes divided by the total number of possible
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outcomes.
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What if you must draw from Bowl A? What's
the probability of drawing a blue marble,
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given that you draw from Bowl A?
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Let's go back to the table and consider only
Bowl A. Bowl A contains 5 marbles of which
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1 is blue, so the probability of picking a
blue one is 1 in 5.
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Notice the probability has changed. In the
first scenario, the sample space consists
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of all 10 marbles, because we are free to
draw from both bowls.
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In the second scenario, we are restricted
to Bowl A. Our new sample space consists of
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only the five marbles in Bowl A. We ignore
these marbles in Bowl.
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Restricting our attention to a specific set
of outcomes changes the sample space, and
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can also change the probability of an event.
This new probability is what we call a conditional
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In the previous example, we calculated the
conditional probability of drawing a blue
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marble, given that we draw from Bowl A.
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This is standard notation for conditional
probability. The vertical bar ( | ) is read
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as "given." The probability we are looking
for precedes the bar, and the condition follows
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the bar.
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Now let's flip things around. Suppose someone
picks a marble at random from either bowl
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A or bowl B and reveals to you that the marble
drawn was blue. What is the probability that
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the blue marble came from Bowl A?
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In other words, what's the conditional probability
that the marble was drawn from Bowl A, given
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that it is blue? Pause the video and try to
work this out.
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Going back to the table, because we are dealing
with the condition that the marble is blue,
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the sample space is restricted to the four
blue marbles.
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Of these four blue marbles, one is in Bowl
A, and each is equally likely to be drawn.
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Thus, the conditional probability is 1 out
of 4.
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Notice that the probability of picking a blue
marble given that the marble came from Bowl
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A is NOT equal to the probability that the
marble came from Bowl A given that the marble
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was blue. Each has a different condition,
so be careful not to mix them up!
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We've seen how tables can help us organize
our data and visualize changes in the sample
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space.
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Let's look at another tool that is useful
for understanding conditional probabilities
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- a tree diagram.
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Suppose we have a jar containing 5 marbles;
2 are blue and 3 are yellow. If we draw any
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one marble at random, the probability of drawing
a blue marble is 2/5.
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Now, without replacing the first marble, draw
a second marble from the jar. Given that the
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first marble is blue, is the probability of
drawing a second blue marble still 2/5?
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NO, it isn't. Our sample space has changed.
If a blue marble is drawn first, you are left
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with 4 marbles; 1 blue and 3 yellow.
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In other words, if a blue marble is selected
first, the probability that you draw blue
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second is 1/4. And the probability you draw
yellow second is 3/4.
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Now pause the video and determine the probabilities
if the yellow marble is selected first instead.
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If a yellow marble is selected first, you
are left with 2 yellow and 2 blue marbles.
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There is now a 2/4 chance of drawing a blue
marble and a 2/4 chance of drawing a yellow
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marble.
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What we have drawn here is called a tree diagram.
The probability assigned to the second branch
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denotes the conditional probability given
that the first happened.
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Tree diagrams help us to visualize our sample
space and reason out probabilities.
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We can answer questions like "What is the
probability of drawing 2 blue marbles in a
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row?" In other words, what is the probability
of drawing a blue marble first AND a blue
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marble second?
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This event is represented by these two branches
in the tree diagram.
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We have a 2/5 chance followed by a 1/4 chance.
We multiply these to get 2/20, or 1/10. The
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probability of drawing two blue marbles in
a row is 1/10.
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Now you do it. Use the tree diagram to calculate
the probabilities of the other possibilities:
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blue, yellow; yellow, blue; and yellow, yellow.
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The probabilities each work out to 3/10. The
four probabilities add up to a total of 1,
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as they should.
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What if we don't care about the first marble?
We just want to determine the probability
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that the second marble is yellow.
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Because it does not matter whether the first
marble is blue or yellow, we consider both
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the blue, yellow, and the yellow, yellow paths.
Adding the probabilities gives us 3/10 + 3/10,
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which works out to 3/5.
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Here's another interesting question. What
is the probability that the first marble drawn
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is blue, given that the second marble drawn
is yellow?
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Intuitively, this seems tricky. Pause the
video and reason through the probability tree
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with a friend.
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Because we are conditioning on the event that
the second marble drawn is yellow, our sample
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space is restricted to these two paths: P(blue,
yellow) and P(yellow, yellow).
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Of these two paths, only the top one meets
our criteria - that the blue marble is drawn
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first.
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We represent the probability as a fraction
of favorable to possible outcomes. Hence,
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the probability that the first marble drawn
is blue, given that the second marble drawn
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is yellow is 3/10 divided by (3/10 +3/10),
which works out to 1/2.
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I hope you appreciate that tree diagrams and
tables make these types of probability problems
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doable without having to memorize any formulas!
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Let's return to our opening question. Recall
that you've tested positive for a cancer that
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afflicts 1 out of 1000 people, based on a
test that is 99% accurate.
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More precisely, out of 100 test results, we
expect about 99 correct results and only 1
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incorrect result.
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Since the test is highly accurate, you might
conclude that the test is unlikely to be wrong,
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and that you most likely have cancer.
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But wait! Let's first use conditional probability
to make sense of our seemingly gloomy diagnosis.
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Now pause the video and determine the probability
that you have the cancer, given that you test
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positive.
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Let's use a tree diagram to help with our
calculations.
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The first branch of the tree represents the
likelihood of cancer in the general population.
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The probability of having the rare cancer
is 1 in 1000, or 0.001. The probability of
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having no cancer is 0.999.
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Let's extend the tree diagram to illustrate
the possible results of the medical test that
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is 99% accurate.
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In the cancer population, 99% will test positive
(correctly), but 1% will test negative (incorrectly).
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These incorrect results are called false negatives.
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In the cancer-free population, 99% will test
negative (correctly), but 1% will test positive
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(incorrectly). These incorrect results are
called false positives.
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Given that you test positive, our sample space
is now restricted to only the population that
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test positive. This is represented by these
two paths.
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The top path shows the probability you have
the cancer AND test positive. The lower path
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shows the probability that you don't have
cancer AND still test positive.
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The probability that you
actually do have the cancer, given that you
test positive, is (0.001*0.99)/((0.001*0.99)+(0.999*0.01)),
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which works out to about 0.09 - less than
10%!
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The error rate of the test is only 1 percent,
but the chance of a misdiagnosis is more than
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90%! Chances are pretty good that you do not
actually have cancer, despite the rather accurate
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test. Why is this so?
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The accuracy of the test actually reflects
the conditional probability that one tests
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positive, given that one has cancer.
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But in practice, what you want to know is
the conditional probability that you have
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cancer, given that you test positive! These
probabilities are NOT the same!
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Whenever we take medical tests, or perform
experiments, it is important to understand
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what events our results are conditioned on,
and how that might affect the accuracy of
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our conclusions.
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In this video, you've seen that conditional
probability must be used to understand and
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predict the outcomes of many events. You've
also learned to evaluate and manage conditional
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probabilities using tables and trees.
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We hope that you will now think more carefully
about the probabilities you encounter, and
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consider how conditioning affects their interpretation.