# 175 Chapter CHAPTER 23: Probability

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3 77 Chapter the first pencil back in the box.] What is the probability that the first pencil you select is red, and the second pencil is green? The probability that the first pencil is red is 4/3 since 4 of the 3 pencils in the box are red. However, when you go to select the second pencil, there are only 2 pencils in the box. So, selecting a green pencil after having selected a red pencil first has a probability of 5/2. So the probability of selecting a red pencil first and then a green pencil is 4/3 5/2 = 20/6. Example 3: Find the probability of selecting 2 pencils from the box above, the first pencil being blue and the second being red. Solution: P (blue and red) = P (blue) P (red, assuming a blue pencil was picked first) = 3/3 4/2 = /3. Here's a very different example from the pen of C. S. Lewis. In an article entitled, "Modern Theology and Biblical Criticism", Lewis discusses his views on some attempts in Biblical criticism to develop a theory of how a certain passage in the Bible came to be written. The particular example Lewis gives is not important for our purposes. Here are his words about the probability involved: "When a critic reconstructs the genesis of a text he usually has to use what may be called linked hypotheses.... Now let us suppose... that the first hypothesis has probability of 90 per cent. But the two together don't still have 90 percent, for the second comes in only on the assumption of the first. You have not A plus B; you have a complex AB. And the mathematicians tell me that AB has only an 8 per cent probability [90% times 90% =.90 x.90 =.8 = 8%-ed.] I'm not good enough at arithmetic to work it out, but you see that if, in a complex reconstruction, you go on thus superinducing hypothesis on hypothesis, you will in the end get a complex in which, though each hypothesis by itself has in a sense a high probability, the whole has almost none. Believe it or not, the probability theory inspired by the study of tossing coins and rolling dice turns out to have very useful results. Here are two examples about which you are likely to hear on the TV news. The first is political polls. As an election approaches, surveys of a relatively small number of voters are used to infer who is currently supported by a majority of the voters. Probability theory allows us to compute how reliable the inferences are. As a second example, consider the testing of a new treatment for a disease. By counting how many people receiving the new treatment are cured, an inference can be made about the probability that the new treatment is an improvement over the old treatment. The idea of probability has also been used in apologetics. For instance, Josh McDowell in Evidence That Demands a Verdict discusses the probability that Messianic prophecies of the Old Testament would be fulfilled in a single person. Quoting another author, he concludes Now these prophecies were either given by inspiration of God or the prophets just wrote them as they thought they should be. In such a case the prophets had just one chance in 0 7 of having them come true in any man, but they all came true in Christ. C.S.Lewis, The Seeing Eye and Other Selected Essays for Christian Reflections, pp 28-29

4 78 Chapter This means that the fulfillment of these eight prophecies alone proves that God inspired the writings of those prophecies to a definiteness which lacks only one chance in 0 7 of being absolute. 2 For a philosophical discussion of this use of probability, see J. P. Moreland, Scaling the Secular City, pp Moreland also briefly discusses Pascal's Wager (pp. 3-32), and it is with an analysis of this argument that we will complete our study of probability. Pascal's Wager First, we need one more concept. It is called "expected value". The terminology might be misleading if you think about the way those words are used in ordinary speech. The technical term is, for our purposes, almost synonymous with the mean which we defined in the previous section. The expected value in a situation is a weighted average, the weights being the probabilities of the various outcomes. For instance, when you roll a die, there are 6 possible outcomes, and they are equally likely. In this case, the expected value would be found by computing: ( 6 ) + ( 2 6 ) + (3 6 ) + (4 6 ) + (5 6 ) + (6 6 ) = 2 6 = 3.5. That is, 3.5 is the "average" result when you roll a die. Of course, you can't actually roll a 3.5, but that's OK; it's an average. So "expected value" does not mean " a value I expect to get". In it's original setting, gambling, expected value had to do with what you could expect to win in a game. For instance, suppose I agree to pay you \$ for every dot that's on the top of the die when you roll it, i.e., if you roll a 5, you get \$5. However, to play the game, you must pay me \$4. On the average, you would expect to win \$3.50 per time you played the game, but after subtracting the \$4 it cost to play, you would net, on the average, -\$.50. That is, I could expect to gain \$.50 on the average, every time I could talk you into playing this game. Granted, you might "get lucky" once in a while, and come out ahead, but in the long run, probability is in my favor. That's why there is very little "gambling" involved in the operation of a casino: the players are gambling, but the casino is almost certain to win big in the long run. Another way to calculate this expected value would be to figure in the cost of the game at the start. This way, you would think of rolling a 5 as actually getting you only \$: -\$4 to play, and then winning \$5, a net of -\$4 + \$5 = \$. Rolling a would mean you would actually lose \$3. Multiplying the net by its probability would look like this: (-\$3 6 ) + ( -\$2 6 ) + (-\$ 6 ) + (\$0 6 ) + (\$ 6 ) + (\$2 6 ) = -\$3 6 = -\$.50. Example 4: A box contains 3 red pencils, 2 green pencils and 0 orange pencils. Consider the following game: without looking, you pick a pencil from the box. If the pencil you pick is green, you get \$3; if it is red, you get \$2. It costs \$ to play this game. Find the expected value. Solution: expected value = expected winnings -- cost = \$3 2 + \$2 3 \$ = \$0.20 or 2 Peter Stoner, quoted in Josh McDowell, Evidence That Demands a Verdict, Here s Life Publishers, San Bernardino, CA, 979, p. 67.

5 79 Chapter expected value = \$2 2 + \$ 3 = \$ ( \$) 0 The concept of expected value is useful in other settings as well. Someone might call these situations "non-gambling" examples, but I'll let you be the judge of that. Consider the situation of a real estate agent. This person agrees to try to sell a house for people wanting to sell it. The real estate agent spends money (and time) working to sell the house, but the owners are only required to pay for these services if, in fact, the house sells. It is typical that the real estate agent gets 6% of the selling price if she sells it by herself, and 3% of the selling price if another agent is involved in the sale (representing the buyer). Suppose a certain real estate agent knows from past experience that she has a 30% chance of selling any house by herself, a 25% chance of selling the house with another agent involved, and a 45% chance of not selling the house. What is the expected value or income if she agrees to try to sell a house for \$350,000? outcome income probability sold by herself \$2, sold with another \$0, not sold \$0.45 expected value = (\$2,000) (.30) + (\$0,500) (.25) + (\$0) (.45) = \$6,300 + \$2,625 + \$0 = \$8,925 What's the significance of an expected value of \$8,925? This is the average amount of income that the agent can "expect" to get from \$350,000 houses. This is helpful to know because it suggests something about how much money the agent would be willing to invest in advertising a house. In some cases (when the house sells), such advertising costs will be easily covered. But if the house does not sell, they are a complete loss. Expected value helps to balance out such occurrences over time. [By the way, this income doesn't typically go directly to the agent. A portion goes to her agency for overhead.] So how did Pascal use this concept of expected value in apologetics? The relevant passage from the Pensees is usually referred to as Pascal's Wager. Students of Pascal have come up with a variety of ways of interpreting and developing Pascal's Wager. For a collection of scholarly essays on the Wager, see Gambling on God, Jeff Jordan, ed., Lanham, MD: Rowman & Littlefield Publ., 994. Pascal presents many arguments in the Pensees to give evidence to the claim that the God of the Bible exists. But for the sake of this argument he assumes that "God exists" and "God does not exist" are equally likely events. That is, in colloquial terms, it is "a toss up". And each of us must bet on the outcome of the toss, i.e., we must chose between the two options in life (that is, we either live as if God exists or as if He doesn't). Unlike a game, however, we cannot choose not to play "Life". In one of the essays in Gambling on God, Thomas Morris writes: In his attempt to recommend a Christian world view to his unbelieving contemporaries, Blaise Pascal hit upon a rare form of argument, an argument that I believe was meant to move them into a better position to have the sort of experience of the reality of God they lacked and of the truth of the Christian message about right relations with God.... Pascal devised an argument to show us that we all ought to bet our lives on God. 3 3 Thomas V. Morris, "Wagering and the Evidence", Gambling on God, p.48.

6 80 Chapter In Pascal's words, "Let us weigh the gain and the loss in wagering that God is." 4 Recalling that the mean or expected value is like a weighted average, Pascal is essentially going to compute the expected value of this "game" when a person bets about God's existence. The gain and loss in this case is not in terms of money, as in our previous examples. Pascal attempts to measure the gain and loss in "happiness". In this analogy, your bet (what you pay to play the game) is how you live your life when you believe that God exists. This is what you will "lose" if God does not exist (the coin comes up tails). Pascal suggests that the amount of "happiness" you would lose is finite at worst. He puts it this way: "Now, what harm will befall you in taking this side? You will be faithful, honest, humble, grateful, generous, a sincere friend, truthful. Certainly you will not have those poisonous pleasures, glory and luxury; but will you not have others?" If it turns out that God exists (the "coin" comes up heads, just like you called it), you win "an eternity of life and happiness". The expected value, then, looks something like this: 6 Bet Probability Payoff Cost Expected Value Christianity.5 Infinite Finite Infinite Atheism.5 Finite Finite Finite Since no matter what finite amount you subtract from infinity you get infinity, Pascal concludes that the expected value is positive infinity. The argument has an obvious conclusion: "If you gain (win), you gain all; if you lose, you lose nothing. Wager, then, without hesitation that He is." 7 "I will tell you that you will thereby gain in this life, and that, at every step you take on this road, you will see so great certainty of gain, so much nothingness in what you risk, that you will at last recognize that you have wagered for something certain and infinite, for which you have given nothing." 8 Pascal concludes this passage with the following statement: If my words please you and seem cogent, you must know that they come from a man who went down upon his knees before and after to pray this infinite and indivisible being [God], to whom he submits his own [being], that he might bring your being also to submit to him for your own good and his glory..." 9 4 Pascal, Pensees, p. 84. Pascal, Pensees, p Thomas V. Morris, Gambling on God, p Pascal, Pensees, p Pascal, Pensees, p Pascal, Pensees, p. 86.

7 8 Chapter 23 8 CHAPTER 23: Probability Homework. Suppose you roll a die. What is the probability you roll a 4? 2. Suppose you roll a die, then roll it again. What is the probability that the first roll is a 3, and the second roll is also a 3? 3. A box contains 3 red pencils, 5 blue pencils, and 7 green pencils. You pick one pencil from the box. Find the probability that it is: a. blue b. green c. red or green d. not red 4. A college student body consists of 300 Caucasians, 300 Asian, 200 Afro-Americans and 300 Hispanics. If a student is selected arbitrarily, what is the probability that the student is: a. Afro-American b. Asian or Caucasian c. not Caucasian d. Hispanic 5. Find the expected value of the number of heads you get when you toss two coins. 6. Consider a game in which you toss 2 coins. You win \$ if you get head and \$3 if you get 2 heads. Find the expected value of this game. 7. Suppose it costs \$2.25 to play the following game: you toss two coins and are paid \$2 for each "head". What is the expected value for this game? 8. Consider a game in which you roll a die. You win \$ if you roll a 4, \$2 if you roll a 5, and \$4 if you roll a 6. What is the expected value for this game? 9. A box contains red pencil, 2 green pencils, and 7 blue pencils. Consider the following game: without looking, pick a pencil from the box; if it is red, you win \$5, and if it is green, you win \$2. Suppose it costs only \$ to play this game. What is its expected value? 0. A box contains 3 red pencils, 4 green pencils, and 3 blue pencils. Consider the following game: without looking, pick a pencil from the box; if it is red, you win \$2, and if it is green, you win \$3. Suppose it costs only \$2 to play this game. What is its expected value?. Suppose drilling an oil well yields oil 2% of the time worth \$2,500,000, yields natural gas 5% of the time worth \$325,000 and is dry (worth nothing) 93% of the time. Suppose it costs \$25,000 to drill an oil well. Find the expected value of a well. 2. Suppose drilling an oil well yields oil 4% of the time worth \$,500,000, yields natural gas 9% of the time worth \$200,000 and is dry (worth nothing) 87% of the time. Suppose it costs \$00,000 to drill an oil well. Find the expected value of a well.

8 82 Chapter Selected Answers a. b. 4. a \$ \$ \$.0. \$4,250 b c. 8 2 c. 0 d. 2 3 d. 2

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