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Consider an urn containing 12 balls, of which 8 are white. A sample of size 4 is to be drawn with replacement (without replacement). What is the conditional probability (in each case) that the first and third balls drawn will be white given that the sample drawn contains exactly 3 white balls?

Short Answer

Expert verified
The conditional probabilities for the first and third balls drawn to be white, given that the sample drawn contains exactly 3 white balls, are: 1. With replacement: \(P(1^{st}W \cap 3^{rd}W | 3W) = \frac{1}{2}\) 2. Without replacement: \(P(1^{st}W \cap 3^{rd}W | 3W) = \frac{13}{22}\)

Step by step solution

01

Case 1: With replacement

When drawing balls with replacement, the total number of possibilities remains the same for each draw, and the events are independent. First, we calculate the denominator: the probability that the sample drawn contains exactly 3 white balls. There are 4 balls to be drawn, so the number of ways to draw exactly 3 white balls and 1 black ball is \(\binom{4}{3} = 4\). Each draw has a probability of \(\frac{8}{12}\) for a white ball and \(\frac{4}{12}\) for a black ball. Thus, the probability of drawing exactly 3 white balls is: \[P(3W) = \binom{4}{3}\left(\frac{8}{12}\right)^3\left(\frac{4}{12}\right) \] Next, we calculate the numerator: the probability that the first and third balls drawn will be white, given that there are exactly 3 white balls in total. Since the first and third balls are white, there are only two possibilities for the remaining 2 positions: (WWBW) and (WBBW). The probabilities for these cases are: \[(WWBW): \left(\frac{8}{12}\right)^3\left(\frac{4}{12}\right)\] \[(WBBW): \left(\frac{8}{12}\right)^2\left(\frac{4}{12}\right)^2 \] Now, we sum these probabilities: \[P(1^{st}W \cap 3^{rd}W | 3W) = \left(\frac{8}{12}\right)^3\left(\frac{4}{12}\right) + \left(\frac{8}{12}\right)^2\left(\frac{4}{12}\right)^2\] We can now calculate the conditional probability: \[P(1^{st}W \cap 3^{rd}W | 3W) = \frac{ \left(\frac{8}{12}\right)^3\left(\frac{4}{12}\right) + \left(\frac{8}{12}\right)^2\left(\frac{4}{12}\right)^2}{ \binom{4}{3}\left(\frac{8}{12}\right)^3\left(\frac{4}{12}\right)}\] By simplifying, we get: \[P(1^{st}W \cap 3^{rd}W | 3W) = \frac{ \left(\frac{2}{3}\right)^3\left(\frac{1}{3}\right) + \left(\frac{2}{3}\right)^2\left(\frac{1}{3}\right)^2}{4\left(\frac{2}{3}\right)^3\left(\frac{1}{3}\right)}\] After simplifying further, we find: \[P(1^{st}W \cap 3^{rd}W | 3W) = \frac{1}{2}.\]
02

Case 2: Without replacement

The probability of drawing exactly 3 white balls without replacement is given by: \[ P(3W) = \frac{\binom{8}{3}\binom{4}{1}}{\binom{12}{4}}\] There are two possibilities for the sequence of balls such that the first and third balls drawn are white, with 3 white balls in total: (WWBW): \[\frac{\binom{7}{1}\binom{4}{1}}{\binom{11}{3}}\] (WBBW): \[\frac{\binom{7}{0}\binom{4}{2}}{\binom{11}{3}}\] Adding the probabilities for both sequences: \[P(1^{st}W \cap 3^{rd}W | 3W) = \frac{\frac{\binom{7}{1}\binom{4}{1}}{\binom{11}{3}} + \frac{\binom{7}{0}\binom{4}{2}}{\binom{11}{3}}}{\frac{\binom{8}{3}\binom{4}{1}}{\binom{12}{4}}}\] Simplifying the expression, we obtain: \[P(1^{st}W \cap 3^{rd}W | 3W) = \frac{13}{22}\] The conditional probabilities in each case are: 1. With replacement: \(\frac{1}{2}\) 2. Without replacement: \(\frac{13}{22}\)

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Key Concepts

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

Probability With Replacement
When we talk about probability with replacement, it means that every time we select an item from a set, we put it back before selecting again. This keeps the total number of items constant, and each event remains independent of the previous ones. For instance, if we draw a ball from an urn and then put it back, the probability of drawing any ball in subsequent trials doesn't change.

In the exercise given, the scenario of drawing balls with replacement implies that picking a white ball on the first draw doesn't affect the likelihood of drawing a white ball on the third draw. This produces a streamlined approach to calculating the conditional probability. The denominator is focused on the overall probability of drawing three white balls out of four, while the numerator is the probability of those white balls appearing in specific positions, such as the first and third draw. This situational analysis ensures a thorough understanding of probability with replacement for students.
Probability Without Replacement
In contrast, probability without replacement means that once an item is selected from a set, it is not returned, thus changing the composition of the set for subsequent selections. In terms of drawing balls from an urn, each draw affects the next because the total number of balls decreases, and the ratio of white to black balls changes if a white ball is not replaced.

In the context of our exercise, if we draw a white ball first, then there are fewer white balls available for the third draw. This impacts the calculation of the conditional probability, as reflected in the different combinatorial expressions for the numerator and denominator in the solution. It is essential to approach probability without replacement problems by considering the changing sample space after each draw and adjusting the calculations accordingly for accurate outcomes.
Combinatorics
Understanding combinatorics is vital when dealing with probability problems. This mathematical field focuses on counting, arranging, and combining items. In probability, it helps quantify the different possible arrangements or selections from a set, which is paramount to calculating probabilities.

The exercise leverages combinatorics through binomial coefficients, denoted by \(\binom{n}{k}\), which represents the number of ways to choose \(k\) items from a set of \(n\) without regard to order. This concept underpins the solution to both parts of the problem: with and without replacement. By utilizing combinatorial reasoning, students can systematically calculate the probabilities of complex events, such as drawing a certain number of white balls from a pool.
Independent Events
The notion of independent events plays a key role in probability theory. Two events are considered independent if the occurrence of one does not affect the probability of the other occurring. Flipping a coin and rolling a die simultaneously are classic examples - the outcome of the coin flip does not influence the die roll.

In the 'with replacement' case of our exercise, each draw constitutes an independent event, as the probability of drawing a white ball remains unchanged after each draw due to the replacement. This allows us to multiply the probabilities of individual events to find the probability of a sequence of independent events happening. It's important for students to clearly distinguish between independent and dependent events to apply the correct methods when calculating probabilities.

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Most popular questions from this chapter

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