Problem 11
Question
Find each probability if a coin is tossed 5 times. \(P(\text { exactly } 2 \text { tails })\)
Step-by-Step Solution
Verified Answer
The probability is \(\frac{5}{16}\).
1Step 1: Understand the Scenario
A coin is tossed 5 times. Each toss results in either heads (H) or tails (T). We want to find the probability of getting exactly 2 tails in these 5 tosses.
2Step 2: Identify the Total Number of Outcomes
Since each coin toss has 2 possible outcomes (H or T), and the coin is tossed 5 times, the total number of possible outcomes is calculated as \(2^5 = 32\).
3Step 3: Determine the Number of Favorable Outcomes
We need to find the number of ways to get exactly 2 tails in 5 tosses. This can be thought of as choosing 2 positions from 5 for tails, and the remaining will be heads. Use the combination formula \(\binom{n}{k}\), where \(n\) is the total number of trials and \(k\) is the number of successful trials (tails in this case). Here, \(\binom{5}{2} = 10\).
4Step 4: Calculate the Probability
The probability of getting exactly 2 tails is the number of favorable outcomes divided by the total number of outcomes. Thus, \[P(\text{exactly 2 tails}) = \frac{\binom{5}{2}}{32} = \frac{10}{32} = \frac{5}{16}\].
Key Concepts
Coin TossBinomial DistributionCombination FormulaOutcome Calculation
Coin Toss
A coin toss is one of the simplest experiments in probability. When you flip a coin, there are two possible results: you either get heads (H) or tails (T). This makes coin tossing a perfect example of a binary outcome experiment. Each flip of the coin is an independent event, meaning the result of one flip does not affect the result of another. In probability terms, this is called an independent trial.
- The probability of landing a head is 0.5.
- The probability of landing a tail is also 0.5.
Binomial Distribution
When we are interested in the outcomes of multiple independent binary trials, such as tossing a coin multiple times, we often use a binomial distribution to model the data. This type of distribution is helpful when we want to know the probability of a certain number of successes (like getting tails) in a fixed number of trials.
- The number of trials (n) is fixed.
- Each trial has two possible outcomes, like success (tails) and failure (heads).
- The probability of success is the same for each trial.
Combination Formula
The combination formula is a powerful tool in probability, especially in binomial distribution problems. It allows us to calculate the number of ways we can choose a subset of items from a larger set, where order does not matter.
For example, in our coin toss problem, we want to know how many ways we can choose exactly 2 tails from 5 tosses. This is represented by the formula \[\binom{n}{k} = \frac{n!}{k!(n-k)!}\]
where:
For example, in our coin toss problem, we want to know how many ways we can choose exactly 2 tails from 5 tosses. This is represented by the formula \[\binom{n}{k} = \frac{n!}{k!(n-k)!}\]
where:
- \(n\) is the total number of items (tosses),
- \(k\) is the number of items to choose (tails).
Outcome Calculation
Calculating the probability of a specific outcome involves understanding both the total possible outcomes and the number of successful outcomes. In coin toss experiments, the total number of outcomes after several tosses is calculated with powers of 2. For 5 tosses, it is \(2^5 = 32\) possible outcomes.
To find the probability of getting 2 tails, we divide the number of successful outcomes (as calculated with the combination formula) by the total number of possible outcomes from the coin tosses. For example, using the choices from the combination formula (10 ways for 2 tails), we calculate:
To find the probability of getting 2 tails, we divide the number of successful outcomes (as calculated with the combination formula) by the total number of possible outcomes from the coin tosses. For example, using the choices from the combination formula (10 ways for 2 tails), we calculate:
- \[ P(\text{exactly 2 tails}) = \frac{\binom{5}{2}}{32} = \frac{10}{32} = \frac{5}{16} \]
Other exercises in this chapter
Problem 11
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