V. Probability. by David M. Lane and Dan Osherson

Transcription

1 V. Probability by David M. Lane and Dan Osherson Prerequisites none F.Introduction G.Basic Concepts I.Gamblers Fallacy Simulation K.Binomial Distribution L.Binomial Demonstration M.Base Rates Probability is an important and complex field of study. Fortunately, only a few basic issues in probability theory are essential for understanding statistics at the level covered in this book. These basic issues are covered in this chapter. The introductory section discusses the definitions of probability. This is not as simple as it may seem. The section on basic concepts covers how to compute probabilities in a variety of simple situations. The Gambler's Fallacy Simulation provides an opportunity to explore this fallacy by simulation. The Birthday Demonstration illustrates the probability of finding two or more people with the same birthday. The Binomial Demonstration shows the binomial distribution for different parameters. The section on base rates discusses an important but oftenignored factor in determining probabilities. It also presents Bays' Theorem. The Bays' Theorem Demonstration shows how a tree diagram and Bays' Theorem result in the same answer. Finally, the Monty Hall Demonstration lets you play a game with a very counterintuitive result. 185

2 Remarks on the Concept of "Probability" by Dan Osherson Prerequisites None Learning Objectives 1. Define symmetrical outcomes 2. Distinguish between frequentist and subjective approaches 3. Determine whether the frequentist of subjective approach is better suited for a given situation Inferential statistics is built on the foundation of probability theory, and has been remarkably successful in guiding opinion about the conclusions to be drawn from data. Yet (paradoxically) the very idea of probability has been plagued by controversy from the beginning of the subject to the present day. In this section we provide a glimpse of the debate about the interpretation of the probability concept. One conception of probability is drawn from the idea of symmetrical outcomes. For example, the two possible outcomes of tossing a fair coin seem not to be distinguishable in any way that affects which side will land up or down. Therefore the probability of heads is taken to be 1/2, as is the probability of tails. In general, if there are N symmetrical outcomes, the probability of any given one of them occurring is taken to be 1/N. Thus, if a six-sided die is rolled, the probability of any one of the six sides coming up is 1/6. Probabilities can also be thought of in terms of relative frequencies. If we tossed a coin millions of times, we would expect the proportion of tosses that came up heads to be pretty close to 1/2. As the number of tosses increases, the proportion of heads approaches 1/2. Therefore, we can say that the probability of a head is 1/2. If it has rained in Seattle on 62% of the last 100,000 days, then the probability of it raining tomorrow might be taken to be This is a natural idea but nonetheless unreasonable if we have further information relevant to whether it will rain tomorrow. For example, if tomorrow is August 1, a day of the year on which it seldom rains in Seattle, we should only consider the percentage of the time it rained on August 1. But even this is not enough since the probability of rain on the next August 1 depends on the humidity. (The chances are higher in the presence of high humidity.) So, we should consult only the prior occurrences of August 1 that had the same humidity as the next occurrence of August 1. Of 186

3 course, wind direction also affects probability... You can see that our sample of prior cases will soon be reduced to the empty set. Anyway, past meteorological history is misleading if the climate is changing? For some purposes, probability is best thought of as subjective. Questions such as "What is the probability that Ms. Garcia will defeat Mr. Smith in an upcoming congressional election?" do not conveniently fit into either the symmetry or frequency approaches to probability. Rather, assigning probability 0.7 (say) to this event seems to reflect the speaker's personal opinion --- perhaps his willingness to bet according to certain odds. Such an approach to probability, however, seems to lose the objective content of the idea of chance; probability becomes mere opinion. Two people might attach different probabilities to the election outcome, yet there would be no criterion for calling one "right" and the other "wrong." We cannot call one of the two people right simply because she assigned higher probability to the outcome that actually transpires. After all, you would be right to attribute probability 1/6 to throwing a six with a fair die, and your friend who attributes 2/3 to this event would be wrong. And you are still right (and your friend is still wrong) even if the die ends up showing a six! The lack of objective criteria for adjudicating claims about probabilities in the subjective perspective is an unattractive feature of it for many scholars. Like most work in the field, the present text adopts the frequentist approach to probability in most cases. Moreover, almost all the probabilities we shall encounter will be nondogmatic, that is, neither zero nor one. An event with probability 0 has no chance of occurring; an event of probability 1 is certain to occur. It is hard to think of any examples of interest to statistics in which the probability is either 0 or 1. (Even the probability that the Sun will come up tomorrow is less than 1.) The following example illustrates our attitude about probabilities. Suppose you wish to know what the weather will be like next Saturday because you are planning a picnic. You turn on your radio, and the weather person says, There is a 10% chance of rain. You decide to have the picnic outdoors and, lo and behold, it rains. You are furious with the weather person. But was she wrong? No, she did not say it would not rain, only that rain was unlikely. She would have been flatly wrong only if she said that the probability is 0 and it subsequently rained. However, if you kept track of her weather predictions over a long period of time 187

4 and found that it rained on 50% of the days that the weather person said the probability was 0.10, you could say her probability assessments are wrong. So when is it accurate to say that the probability of rain is 0.10? According to our frequency interpretation, it means that it will rain 10% of the days on which rain is forecast with this probability. 188

5 Basic Concepts by David M. Lane Prerequisites Introduction to Probability Learning Objectives 1. Compute probability in a situation where there are equally-likely outcomes 2. Apply concepts to cards and dice 3. Compute the probability of two independent events both occurring 4. Compute the probability of either of two independent events occurring 5. Do problems that involve conditional probabilities 6. Compute the probability that in a room of N people, at least two share a birthday 7. Describe the gamblers' fallacy Probability of a Single Event If you roll a six-sided die, there are six possible outcomes, and each of these outcomes is equally likely. A six is as likely to come up as a three, and likewise for the other four sides of the die. What, then, is the probability that a one will come up? Since there are six possible outcomes, the probability is 1/6. What is the probability that either a one or a six will come up? The two outcomes about which we are concerned (a one or a six coming up) are called favorable outcomes. Given that all outcomes are equally likely, we can compute the probability of a one or a six using the formula: = " " " " " " "! In this case there are two favorable outcomes and six possible outcomes. So the probability of throwing either a one or six is 1/3. Don't be misled by our use of the term "favorable," by the way. You should understand it in the sense of "favorable to the event in question happening." That event might not be favorable to your wellbeing. You might be betting on a three, for example. 189

6 The above formula applies to many games of chance. For example, what is the probability that a card drawn at random from deck of playing cards will be an ace? Since the deck has four aces, there are four favorable outcomes; since the deck has 52 cards, there are 52 possible outcomes. The probability is therefore 4/52 = 1/13. What about the probability that the card will be a club? Since there are 13 clubs, the probability is 13/52 = 1/4. Let's say you have a bag with 20 cherries, 14 sweet and 6 sour. If you pick a cherry at random, what is the probability that it will be sweet? There are 20 possible cherries that could be picked, so the number of possible outcomes is 20. Of these 20 possible outcomes, 14 are favorable (sweet), so the probability that the cherry will be sweet is 14/20 =7/10. There is one potential complication to this example, however. It must be assumed that the probability of picking any of the cherries is the same as the probability of picking any other. This wouldn't be true if (let us imagine) the sweet cherries are smaller than the sour ones. (The sour cherries would come to hand more readily when you sampled from the bag.) Let us keep in mind, therefore, that when we assess probabilities in terms of the ratio of favorable to all potential cases, we rely heavily on the assumption of equal probability for all outcomes. Here is a more complex example. You throw 2 dice. What is the probability that the sum of the two dice will be 6? To solve this problem, list all the possible outcomes. There are 36 of them since each die can come up one of six ways. The 36 possibilities are shown below. Table possible outcomes. Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total

7 You can see that 5 of the 36 possibilities total 6. Therefore, the probability is 5/36. If you know the probability of an event occurring, it is easy to compute the probability that the event does not occur. If P(A) is the probability of Event A, then 1 - P(A) is the probability that the event does not occur. For the last example, the probability that the total is 6 is 5/36. Therefore, the probability that the total is not 6 is 1-5/36 = 31/

8 Gamblers Fallacy A fair coin is flipped five times and comes up heads each time. What is the probability that it will come up heads on the sixth flip? The correct answer is, of course, 1/2. But many people believe that a tail is more likely to occur after throwing five heads. Their faulty reasoning may go something like this "In the long run, the number of heads and tails will be the same, so the tails have some catching up to do." The flaws in this logic are exposed in the simulation in this chapter. 196

9 IX. Sampling Distributions by David M. Lane Prerequisites none A.Introduction B.Sampling Distribution of the Mean C.Sampling Distribution of Difference Between Means The concept of a sampling distribution is perhaps the most basic concept in inferential statistics. It is also a difficult concept to teach because a sampling distribution is a theoretical distribution rather than an empirical distribution. The introductory section defines the concept and gives an example for both a discrete and a continuous distribution. It also discusses how sampling distributions are used in inferential statistics. The Basic Demo is an interactive demonstration of sampling distributions. It is designed to make the abstract concept of sampling distributions more concrete. The Sample Size Demo allows you to investigate the effect of sample size on the sampling distribution of the mean. The remaining sections of the chapter concern the sampling distributions of important statistics: the Sampling Distribution of the Mean, the Sampling Distribution of the Difference Between Means. 279

10 Introduction to Sampling Distributions by David M. Lane Prerequisites Distributions Inferential Statistics Learning Objectives 1. Define inferential statistics 2. Graph a probability distribution for the mean of a discrete variable 3. Describe a sampling distribution in terms of "all possible outcomes." 4. Describe a sampling distribution in terms of repeated sampling 5. Describe the role of sampling distributions in inferential statistics 6. Define the standard error of the mean Suppose you randomly sampled 10 people from the population of women in Houston Texas between the ages of 21 and 35 years and computed the mean height of your sample. You would not expect your sample mean to be equal to the mean of all women in Houston. It might be somewhat lower or it might be somewhat higher, but it would not equal the population mean exactly. Similarly, if you took a second sample of 10 people from the same population, you would not expect the mean of this second sample to equal the mean of the first sample. Recall that inferential statistics concern generalizing from a sample to a population. A critical part of inferential statistics involves determining how far sample statistics are likely to vary from each other and from the population parameter. (In this example, the sample means are sample statistics and the sample parameter is the population mean.) As the later portions of this chapter show, these determinations are based on sampling distributions. Discrete Distributions We will illustrate the concept of sampling distributions with a simple example. Figure 1 shows three pool balls, each with a number on it. Two of the balls are selected randomly (with replacement) and the average of their numbers is computed. 280

11 1 2 3 Figure 1. The pool balls. All possible outcomes are shown below in Table 1. Table 1. All possible outcomes when two balls are sampled. Outcome Ball 1 Ball 2 Mean Notice that all the means are either 1.0, 1.5, 2.0, 2.5, or 3.0. The frequencies of these means are shown in Table 2. The relative frequencies are equal to the frequencies divided by nine because there are nine possible outcomes. Table 2. Frequencies of means for N = 2. Mean Frequency Relative Frequency

12 Figure 2 shows a relative frequency distribution of the means based on Table 2. This distribution is also a probability distribution since the Y-axis is the probability of obtaining a given mean from a sample of two balls in addition to being the relative frequency Relative(Frequency((Probability) Mean Figure 2. Distribution of means for N = 2. The distribution shown in Figure 2 is called the sampling distribution of the mean. Specifically, it is the sampling distribution of the mean for a sample size of 2 (N = 2). For this simple example, the distribution of pool balls and the sampling distribution are both discrete distribution. The pool balls have only the numbers 1, 2, and 3, and a sample mean can have one of only five possible values. 282

13 There is an alternative way of conceptualizing a sampling distribution that will be useful for more complex distributions. Imagine that two balls are sampled (with replacement) and the mean of the two balls is computed and recorded. Then this process is repeated for a second sample, a third sample, and eventually thousands of a samples. After thousands of samples are taken and the mean computed for each, a relative frequency distribution is drawn. The more samples, the closer the relative frequency distribution will come to the sampling distribution shown in Figure 2. As the number of samples approaches infinity, the frequency distribution will approach the sampling distribution. This means that you can conceive of a sampling distribution as being a frequency distribution based on a very large number of samples. To be strictly correct, the sampling distribution only equals the frequency distribution exactly when there is an infinite number of samples. It is important to keep in mind that every statistic, not just the mean has a sampling distribution. For example, Table 3 shows all possible outcomes for the range of two numbers (larger number minus the smaller number). Table 4 shows the frequencies for each of the possible ranges and Figure 3 shows the sampling distribution of the range. Table 3. All possible outcomes when two balls are sampled. Outcome Ball 1 Ball 2 Range

14 Table 4. Frequencies of ranges for N = 2. Range Frequency Relative Frequency Rela ve&frequency&(probability) Range Figure 3. Distribution of ranges for N = 2. It is also important to keep in mind that there is a sampling distribution for various sample sizes. For simplicity, we have been using N = 2. The sampling distribution of the range for N = 3 is shown in Figure

15 Relative(Frequency((Probability) Range Figure 4. Distribution of ranges for N =

16 Sampling Distributions and Inferential Statistics As we stated in the beginning of this chapter, sampling distributions are important for inferential statistics. In the examples given so far, a population was specified and the sampling distribution of the mean and the the range were determined. In practice, the process proceeds the other way: you collect sample data and, from these data you estimate parameters of the sampling distribution. This knowledge of the sampling distribution can be very useful. For example, knowing the degree to which means from different samples would differ from each other and from the population mean would give you a sense of how close your particular sample mean is likely to be to the population mean. Fortunately, this information is directly available from a sampling distribution. The most common measure of how much sample means differ from each other is the standard deviation of the sampling distribution of the mean. This standard deviation is called the standard error of the mean. If all the sample means were very close to the population mean, then the standard error of the mean would be small. On the other hand, if the sample means varied considerably, then the standard error of the mean would be large. To be specific, assume your sample mean were 125 and you estimated that the standard error of the mean were 5 (using a method shown in a later section). If you had a normal distribution, then it would be likely that your sample mean would be within 10 units of the population mean since most of a normal distribution is within two standard deviations of the mean. 286

17 Sampling Distribution of the Mean by David M. Lane Prerequisites Introduction to Sampling Distributions Variance Sum Law I Learning Objectives 1. State the mean and variance of the sampling distribution of the mean 2. Compute the standard error of the mean 3. State the central limit theorem The sampling distribution of the mean was defined in the section introducing sampling distributions. This section reviews some important properties of the sampling distribution of the mean that were introduced in the demonstrations in this chapter. Mean The mean of the sampling distribution of the mean is the mean of the population from which the scores were sampled. Therefore, if a population has a mean, μ, then the sampling distribution of the mean is also μ. The symbol μm is used to refer to the mean of the sampling distribution of the mean. Therefore, the formula for the mean of the sampling distribution of the mean can be written as: expected mean of sample µm = µ 287

18 The standard error of the mean is the standard deviation of the sampling distribution of the mean. It is therefore the square root of the variance of the sampling distribution of the mean and can be written as: = The standard error is represented by a σ because it is a standard deviation. The subscript(m) indicates that the standard error in question is the standard error of the mean. 288

19 Figure 1. A simulation of a sampling distribution. The parent population is uniform. The blue line under "16" indicates that 16 is the mean. The red line extends from the mean plus and minus one standard deviation. Figure 2 shows how closely the sampling distribution of the mean approximates a normal distribution even when the parent population is very non-normal. If you look closely you can see that the sampling distributions do have a slight positive skew. The larger the sample size, the closer the sampling distribution of the mean would be to a normal distribution. 289

20 Figure 2. A simulation of a sampling distribution. The parent population is very non-normal. 290