6.3 Probabilities with Large Numbers

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1 6.3 Probabilities with Large Numbers In general, we can t perfectly predict any single outcome when there are numerous things that could happen. But, when we repeatedly observe many observations, we expect the distribution of the observed outcomes to show some type of pattern or regularity. 1

2 Tossing a coin One flip It s a chance whether it s a head or tail. 100 flips I can t predict perfectly, but I m not going to predict 0 tails, that s just not likely to happen. I m going to predict something close to 50 tails and 50 heads. That s much more likely than 0 tails or 0 heads. 2

3 Tossing a coin many times Let represent the proportion of heads that I get when I toss a coin many times. pˆ If I toss 45 heads on 100 flips, then ˆp = = 0.45 pˆ is pronounced p-hat. It is the relative frequency of heads in this example. If I toss 48 heads on 100 flips, then ˆp = =

4 Tossing a coin many times I expect (the proportion of heads) to be somewhere near 50% or pˆ What if I only toss a coin two times? The only possible values for 1) pˆ = 0/2 = ) pˆ = 1/2 = 0.50 pˆ 3) = 2/2 = 1.00 pˆ are Pretty far from the true probability of flipping a head on a fair coin (0.5). 4

5 Tossing a coin many MANY times It turns out If I toss it 100 times I expect to be near 0.50 If I toss it 1000 times I expect to be even nearer to 0.50 If I toss it 10,000 times I expect to be even nearer to 0.50 than in the 1000 coin toss. 5

6 Tossing a coin many MANY times This shows a very possible observed situation Toss it 100 times, 45 heads. Toss it 1000 times, 485 heads. Toss it 10,000 times, 4955 heads. ˆp = = ˆp = = ˆp = = With more tosses, the closer gets to the truth of 0.50 (it s zero-ing in on the truth). pˆ 6

7 Law of Large Numbers For repeated independent trials, the long run (i.e. after many many trials) relative frequency of an outcome gets closer and closer to the true probability of the outcome. If you re using your trials to estimate a probability (i.e. empirical probability), you ll do a better job at estimating by using a larger number of trials. 7

8 Computer simulation of rolling a die. We ll keep track of the proportion of 1 s. Number of rolls getting larger 8

9 Law of Large Numbers Suppose you don t know if a coin is fair. pˆ pˆ Let p represent the true probability of a head. from n=10 trials is an OK estimate for p. from n=1000 trials is a better estimate for p. 6 p ˆ = = p ˆ = = pˆ from n=10,000 trials is an even better estimate for. p 5014 p ˆ = =

10 Law of Large Numbers (LLN) In the coin flip example, your estimate with n=10 may hit the truth right on the nose, but you have a better chance of pˆ being very close to p = 0.5 when you have a much larger n. 10

11 Law of Large Numbers (LLN) LLN let s insurance companies do a pretty good job of estimating costs in the coming year for large groups (i.e. when they have LOTS and LOTS of insurees). Hard to predict for one person, but we can do a pretty good job of predicting total costs or total proportion of people who will have an accident for a group. 11

12 Example: The Binary Computer Company manufactures computer chips used in DVD players. Those chips are made with 0.73 defective rate. A) When one chip is drawn, list the possible outcomes. B) If one chip is randomly selected, find the probability that it is good. C) If you select 100,000 chips, how many defects should you expect? 12

13 Answers: A) Two possible outcomes: defective or good. B) P(good)=1-P(defective) = =0.27 C) If you select 100,000 chips, how many defects should you expect? As the number of chips sampled gets larger, the proportion of defects in the sample approaches the true proportion of defects (which is 0.73). So, with this large of a sample, I would expect about 73% of the sample to be defects, or 0.73 x 100,000= 73,000. I used the Law of Large numbers in the above. 13

14 Expected Value Game based on the roll a die: If a 1 or 2 is thrown, the player gets \$3. If a 3, 4, 5, or 6 is thrown, the house wins (you get nothing). Would you play if it cost \$5 to join the game? Would you play if it cost \$1 to join the game? What do you EXPECT to gain (or lose) from playing? We ll return to this example later 14

15 Expected Value There are alternative ways to compute expected values (all with the same result). I will focus on those where the probabilities of the possible events sum to 1. 15

16 Expected Value Life insurance companies depend on the law of large numbers to stay solvent (i.e. be able to pay their debts). I pay \$1000 annually for life insurance for a \$500,000 policy (in the event of a death). Suppose they only insured me. If I live (high probability), they make \$1000. If I die (low probability), they lose \$499,000!! Should they gamble that I m not going to die? Not sound business practice. 16

17 Expected Value It s hard to predict the outcome for one person, but much easier to predict the average of outcomes among a group of people. Insurance is based on the probability of events (death, accidents, etc.), some of which are very unlikely. 17

18 Expected Value For the upcoming year, suppose: P(die) = 1/500 = P(live) = 499/500 = A life insurance policy costs \$100 and pays out \$10,000 in the event of a death If the company insures a million people, what do they expect to gain (or lose)? 18

19 Expected Value For each policy, one of two things can happen a payout (die) or no payout (live). If the person dies and they pay out, they lose \$9,900 on the policy. (they did collect the \$100 regardless of death or not) If the person lives and there s no payout, they gain \$100 on the policy. Expected Value = (1/500)*(-\$9,900)+(499/500)*(\$100) = \$80 (per policy) This amounts to a profit of \$80,000,000 on sales of 1 million policies 19

20 Expected Value (per policy) Probability of a loss Probability of a gain Expected Value = (1/500)*(-\$9,900)+(499/500)*(\$100) = \$80 (per policy) The negative loss The positive gain This amounts to a profit of \$80,000,000 on sales of 1 million policies (overall expected value of \$80,000,000). 20

21 Expected Value For the upcoming year, suppose: P(die) = 1/500 = P(live) = 499/500 = A life insurance policy costs \$1000 and pays out \$500,000 in the event of a death Expected Value = (1/500)*(-\$499,000)+(499/500)*(\$1000) = \$0 (per policy) They expect to just break even (if more people die than expected, they re in trouble). Charging any less would result in an expected loss. 21

22 Expected Value The law of large numbers comes into play for insurance companies because their actual payouts will depend on the observed number of deaths. As they observe (or insure) more people, the relative frequency of a death will get closer and closer to the 1/500 probability on which they based their calculations. 22

23 Calculating Expected Value Consider two events, each with its own value and probability. The expected value is expected value = (value of event 1) * (probability of event 1) + (value of event 2) * (probability of event 2) This formula can be extended to any number of events by including more terms in the sum. Copyright 2009 Pearson Education, Inc. 23

24 Expected Value Game based on the roll a die: If a 1 or 2 is thrown, the player gets \$3. If a 3, 4, 5, or 6 is thrown, the house wins (you get nothing). If the game costs \$5 to play, what is the expected value of a game? Expected Value = (2/6)*(-\$2)+(4/6)*(-\$5) = -\$4 (per game) No matter what is rolled, you re losing money. 24

25 Expected Value Game based on the roll a die: If a 1 or 2 is thrown, the player gets \$3. If a 3, 4, 5, or 6 is thrown, the house wins (you get nothing). If the game costs \$1 to play, what is the expected value of a game? Expected Value = (2/6)*(\$2)+(4/6)*(-\$1) = \$0 (per game) This is a fair game. 25

26 Expected Value Definition The expected value of a variable is the weighted average of all its possible events. Because it is an average, we should expect to find the expected value only when there are a large number of events, so that the law of large numbers comes into play. For the life insurance company, the observed relative frequency of deaths approaches the truth (which is 1/500) as they get more and more insurees. Copyright 2009 Pearson Education, Inc. 26

27 Expected Value Game based on the roll of two dice: If a sum of 12 is thrown, the player gets \$40. If anything else is thrown, the house wins (you get nothing). If the game costs \$4 to play, what is the expected value of a game? Expected Value = (1/36)*(\$36)+(35/36)*(-\$4) (per game) = - \$2.89 This is a unfair game (for the gambler). 27

28 Everyone who bets any part of his fortune, however small, on a mathematically unfair game of chance acts irrationally -- Daniel Bernoulli, 18 th century mathematician In the long run, you know you ll lose. But perhaps you think you can beat the house in the short run. 28

29 The Gambler s Fallacy Definition The gambler s fallacy is the mistaken belief that a streak of bad luck makes a person due for a streak of good luck. Copyright 2009 Pearson Education, Inc. 29

30 Game based on the toss of a fair coin: Win \$1 for heads and lose \$1 for tails (no cost to play). Expected Value = (1/2)*(\$1)+(1/2)*(-\$1) (per game) = \$0 After playing 100 times, you have 45 heads and 55 tails (you re down \$10). Thinking things will balance out in the end you keep playing until you ve played 1000 times. Unfortunately, you now have 480 heads and 520 tails (you re down \$40). 30

31 Do these results go against what we know about the law of large numbers? Nope. ˆp n=100 = = ˆp n=1000 = = The proportion of heads DID get closer to 0.50 But the difference between the number of heads and tails got larger, which is reasonable as the number of tosses gets larger. 31

32 Outcomes of coin tossing trials Difference Number Number Number Percentage between number of of tosses of tails of heads of heads heads and tails % % 60 10,000 5,050 4, % ,000 50,100 49, % 200 Though the percentage of heads gets closer to 50%, the difference in the number of heads and tails doesn t get closer to zero (and the difference is what relates to the money in your pocket). 32

33 Streaks When a sequence of events gives rise to a streak, this can also lend itself to the gambler s fallacy. Flip a coin 7 times: HHHHHHH But we know that the above streak is just as likely as HTTHTTH because these are equally likely outcomes. Perhaps you think a tail is due next in the first streak, but P(tail)=P(head)=0.50 no matter what you tossed in the past. 33

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