/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Problem 57 The National Public Radio show C... [FREE SOLUTION] | 91Ó°ÊÓ

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The National Public Radio show Car Talk has a feature called "The Puzzler." Listeners are asked to send in answers to some puzzling questions-usually about cars but sometimes about probability (which, of course, must account for the incredible popularity of the program!). Suppose that for a car question, 800 answers are submitted, of which 50 are correct. a. Suppose that the hosts randomly select two answers from those submitted with replacement. Calculate the probability that both selected answers are correct. (For purposes of this problem, keep at least five digits to the right of the decimal.) b. Suppose now that the hosts select the answers at random but without replacement. Use conditional probability to evaluate the probability that both answers selected are correct. How does this probability compare to the one computed in Part (a)?

Short Answer

Expert verified
The probability that both answers are correct with replacement is \(\frac{1}{256}\) or \(0.00391\) and the probability without replacement is \(\frac{49}{12784}\) or \(0.00383\).

Step by step solution

01

Calculate the Probability with Replacement

The total number of answers submitted is 800, and the number of correct answers submitted is 50. The probability of selecting a correct answer on a given attempt - \(P(Correct)\) - is therefore \(\frac{50}{800}\), which simplifies to \(\frac{1}{16}\). When replacement is involved, the probabilities don't change with each attempt because the total number of answers remains the same. Therefore, the probability of getting two correct answers when picking with replacement is the product of the probabilities of picking a correct answer twice \((\frac{1}{16})^2\), which equals \(\frac{1}{256}\).
02

Calculate the Probability without Replacement

When picking without replacement, the pool of answers decreases by one each time an answer is selected. The probability of picking a correct answer on the first attempt - \(P(Correct1)\) - remains \(\frac{50}{800}\) or \(\frac{1}{16}\). However, the probability of picking a correct answer on the second attempt - \(P(Correct2 | Correct1)\) - is \(\frac{49}{799}\), as both the numerator and the denominator decrease by one. The probability of picking two correct answers without replacement is the product of these probabilities, \(\frac{1}{16} * \frac{49}{799} = \frac{49}{12784}\).
03

Compare the Two Probabilities

By calculating the decimal equivalent of both probabilities, we find that the probability with replacement is \(0.00391\) while the probability without replacement is \(0.00383\). Though they are close, it is slightly more likely to get two correct answers when replacing after each pick than when not replacing.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Conditional Probability
Conditional probability helps us determine the likelihood of an event occurring given a certain condition. In the car question, if the first answer selected is correct, the probability of the second answer also being correct changes depending on whether or not the first answer is replaced.
When dealing with events without replacement, the probability of the second event is dependent on the outcome of the first event. For instance, if you've already picked a correct answer once and do not replace it, there is one less correct answer available in the pool. This is why conditional probability is so essential to understand in these scenarios.
  • Formula for conditional probability is: \( P(A|B) = \frac{P(A \cap B)}{P(B)} \)
  • "\(P(A|B)\)" reads as the probability of event A given event B has occurred.
  • It adjusts the sample space based on prior outcomes.
Replacement vs. Non-Replacement
Replacement and non-replacement are critical concepts when dealing with probabilities, as they define whether the chosen item is placed back into the sample pool after being selected.
  • With Replacement: The probability for each selection remains constant because the sample pool does not change. For example, if you select a correct answer and replace it, the next choice has the same probability of being correct.
  • Without Replacement: The sample pool changes with each selection, meaning that the probabilities for subsequent selections are altered. When you pick a correct answer and do not replace it, there is one less correct answer available, decreasing the probability of picking another correct answer.

In our exercise, this distinction affects how we calculate the likelihood of getting two correct answers when choosing with or without replacement.
Calculating Probabilities
To calculate probabilities, we need to determine how likely an event is to happen relative to the total number of possible outcomes. This involves simple division of favorable outcomes by the total outcomes.
For example, in the exercise, we have 800 total answers and 50 correct answers. Thus, the probability of picking a correct answer on your first try is \( \frac{50}{800} = \frac{1}{16} \).
  • With Replacement: The probability for both selections is the product of picking a correct answer each time, i.e., \( \left(\frac{1}{16}\right)^2 = \frac{1}{256} \).
  • Without Replacement: The probability changes after the first pick. If the first answer is correct, only 49 correct answers remain out of 799 total, making the probability for the second correct pick \( \frac{49}{799} \). The combined probability then is \( \frac{1}{16} \times \frac{49}{799} = \frac{49}{12784} \).

Taking the decimal form helps in comparison, where we found the probability of both correct picks with replacement as 0.00391 and without replacement as 0.00383. This subtle difference is important for understanding how these two concepts affect outcome probabilities.

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Most popular questions from this chapter

Only \(0.1 \%\) of the individuals in a certain population have a particular disease (an incidence rate of .001). Of thóse whò have the disease, \(95 \%\) test possitive whèn a certain diagnostic test is applied. Of those who do not have the disease, \(90 \%\) test negative when the test is applied. Suppose that an individual from this population is randomly selected and given the test. a. Construct a tree diagram having two first-generation branches, for has disease and doesn't have disease, and two second-generation branches leading out from each of these, for positive test and negative test. Then enter appropriate probabilities on the four branches. b. Use the general multiplication rule to calculate \(P\) (has disease and positive test). c. Calculate \(P\) (positive test). d. Calculate \(P\) (has disease| positive test). Does the result surprise you? Give an intuitive explanation for the size of this probability.

Suppose that a box contains 25 light bulbs, of which 20 are good and the other 5 are defective. Consider randomly selecting three bulbs without replacement. Let \(E\) denote the event that the first bulb selected is good, \(F\) be the event that the second bulb is good, and \(G\) represent the event that the third bulb selected is good. a. What is \(P(E)\) ? b. What is \(P(F \mid E)\) ? c. What is \(P(G \mid E \cap F)\) ? d. What is the probability that all three selected bulbs are good?

A family consisting of three people- \(\mathrm{P}_{1}, \mathrm{P}_{2}\), and \(\mathrm{P}_{3}\) -belongs to a medical clinic that always has a physician at each of stations 1,2, and \(3 .\) During a certain week, each member of the family visits the clinic exactly once and is randomly assigned to a station. One experimental outcome is \((1,2,1)\), which means that \(\mathrm{P}_{1}\) is assigned to station \(1, \mathrm{P}_{2}\) to station 2, and \(\mathrm{P}_{3}\) to station \(1 .\) a. List the 27 possible outcomes. (Hint: First list the nine outcomes in which \(\mathrm{P}_{1}\) goes to station 1, then the nine in which \(\mathrm{P}_{1}\) goes to station 2, and finally the nine in which \(\mathrm{P}_{1}\) goes to station 3 ; a tree diagram might help.) b. List all outcomes in the event \(A\), that all three people go to the same station. c. List all outcomes in the event \(B\), that all three people go to different stations. d. List all outcomes in the event \(C\), that no one goes to station 2 . e. Identify outcomes in each of the following events: \(B^{C}, C^{C}, A \cup B, A \cap B, A \cap C\).

The paper "Good for Women, Good for Men, Bad for People: Simpson's Paradox and the Importance of Sex-Spedfic Analysis in Observational Studies" (Journal of Women's Health and Gender-Based Medicine [2001]: \(867-872\) ) described the results of a medical study in which one treatment was shown to be better for men and better for women than a competing treatment. However, if the data for men and women are combined, it appears as though the competing treatment is better. To see how this can happen, consider the accompanying data tables constructed from information in the paper. Subjects in the study were given either Treatment \(\mathrm{A}\) or Treatment \(\mathrm{B}\), and survival was noted. Let \(S\) be the event that a patient selected at random survives, \(A\) be the event that a patient selected at random received Treatment \(\mathrm{A}\), and \(B\) be the event that a patient selected at random received Treatment \(\mathrm{B}\). a. The following table summarizes data for men and women combined: $$ \begin{array}{l|ccc} & \text { Survived } & \text { Died } & \text { Total } \\ \hline \text { Treatment A } & 215 & 85 & \mathbf{3 0 0} \\ \text { Treatment B } & 241 & 59 & \mathbf{3 0 0} \\ \text { Total } & \mathbf{4 5 6} & \mathbf{1 4 4} & \\ \hline \end{array} $$ i. Find \(P(S)\). ii. Find \(P(S \mid A)\). iii. Find \(P(S \mid B)\). iv. Which treatment appears to be better? b. Now consider the summary data for the men who participated in the study: $$ \begin{array}{l|rrr} & \text { Survived } & \text { Died } & \text { Total } \\ \hline \text { Treatment A } & 120 & 80 & \mathbf{2 0 0} \\ \text { Treatment B } & 20 & 20 & 40 \\ \text { Total } & \mathbf{1 4 0} & \mathbf{1 0 0} & \\ \hline \end{array} $$ i. Find \(P(S)\). ii. Find \(P(S \mid A)\). iii. Find \(P(S \mid B)\). iv. Which treatment appears to be better? c. Now consider the summary data for the women who participated in the study: $$ \begin{array}{l|rrc} & \text { Survived } & \text { Died } & \text { Total } \\ \hline \text { Treatment A } & 95 & 5 & \mathbf{1 0 0} \\ \text { Treatment B } & 221 & 39 & \mathbf{2 6 0} \\ \text { Total } & \mathbf{3 1 6} & \mathbf{1 4 4} & \\ \hline \end{array} $$ i. Find \(P(S)\). ii. Find \(P(S \mid A)\). iii. Find \(P(S \mid B)\). iv. Which treatment appears to be better? d. You should have noticed from Parts (b) and (c) that for both men and women, Treatment \(A\) appears to be better. But in Part (a), when the data for men and women are combined, it looks like Treatment \(\mathrm{B}\) is better. This is an example of what is called Simpson's paradox. Write a brief explanation of why this apparent inconsistency occurs for this data set. (Hint: Do men and women respond similarly to the two treatments?)

A shipment of 5000 printed circuit boards contains 40 that are defective. Two boards will be chosen at random, without replacement. Consider the two events \(E_{1}=\) event that the first board selected is defective and \(E_{2}=\) event that the second board selected is defective. a. Are \(E_{1}\) and \(E_{2}\) dependent events? Explain in words. b. Let \(n o t E_{1}\) be the event that the first board selected is not defective (the event \(E_{1}^{C}\) ). What is \(P\left(\right.\) not \(E_{1}\) )? c. How do the two probabilities \(P\left(E_{2} \mid E_{1}\right)\) and \(P\left(E_{2} \mid\right.\) not \(\left.E_{1}\right)\) compare? d. Based on your answer to Part (c), would it be reasonable to view \(E_{1}\) and \(E_{2}\) as approximately independent?

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