The population proportion is 0.20. What is the probability that a sample proportion will be within 10.04 of the population proportion for each of the following sample sizes? (Round your answers to 4 decimal places.) (a) n = 100 (b) n = 200 (c) n = 500 (d) n = 1,000 X (e) What is the advantage of a larger sample size? O There is a higher probability will be within 10.04 of the population standard deviation. O There is a higher probability p will be within +0.04 of the population proportion p. O We can guarantee p will be within ±0.04 of the population proportion p. O As sample size increases, E(p) approaches p.

Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter12: Probability
Section12.4: Discrete Random Variables; Applications To Decision Making
Problem 21E
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### Understanding Sampling Probability in Population Proportions

The population proportion is given as \( p = 0.20 \). The task is to determine the probability that a sample proportion will fall within ±0.04 of the population proportion for the given sample sizes. The answers should be rounded to four decimal places.

**Sample Sizes and Probability Computations:**

(a) \( n = 100 \)
- [Answer to be computed]

(b) \( n = 200 \)
- [Answer to be computed]

(c) \( n = 500 \)
- [Answer to be computed]

(d) \( n = 1,000 \)
- [Answer to be computed]

**Interpreting Larger Sample Sizes:**
(e) What is the advantage of a larger sample size?
   
Possible options are:
1. There is a higher probability \(\sigma_{\bar{p}}\) will be within ±0.04 of the population standard deviation.
2. There is a higher probability \(\bar{p}\) will be within ±0.04 of the population proportion \( p \).
3. We can guarantee \(\bar{p}\) will be within ±0.04 of the population proportion \( p \).
4. As sample size increases, \( E(\bar{p}) \) approaches \( p \).

### Detailed Explanation

**Understanding Graphs and Diagrams:**
This section contains text boxes and radio buttons for inputting sample probabilities and selecting the correct statements. There are no visual graphs or complex diagrams, but the information looks to be organized in a Q&A format for educational purposes.

**Instruction on Calculating Probabilities:**
To solve for each part (a-d), one would typically standardize the sampling distribution of the sample portion \(\hat{p}\) using:
\[ \hat{p} \text{ is approximately } N(p, \sqrt{\frac{p(1-p)}{n}}) \]
- Calculate the standard error (SE) for each sample size \( n \).

Using the Standard Normal Distribution (Z-distribution), center it around
\[ Z = \frac{\hat{p} - p}{\sqrt{\frac{p (1 - p)}{n}}} \]

By applying this standard, you can determine the values to plug in to calculate each probability and answer the sample size advantage question.

This framework helps in understanding how sample size influences the precision of estimates in statistical analysis.
Transcribed Image Text:### Understanding Sampling Probability in Population Proportions The population proportion is given as \( p = 0.20 \). The task is to determine the probability that a sample proportion will fall within ±0.04 of the population proportion for the given sample sizes. The answers should be rounded to four decimal places. **Sample Sizes and Probability Computations:** (a) \( n = 100 \) - [Answer to be computed] (b) \( n = 200 \) - [Answer to be computed] (c) \( n = 500 \) - [Answer to be computed] (d) \( n = 1,000 \) - [Answer to be computed] **Interpreting Larger Sample Sizes:** (e) What is the advantage of a larger sample size? Possible options are: 1. There is a higher probability \(\sigma_{\bar{p}}\) will be within ±0.04 of the population standard deviation. 2. There is a higher probability \(\bar{p}\) will be within ±0.04 of the population proportion \( p \). 3. We can guarantee \(\bar{p}\) will be within ±0.04 of the population proportion \( p \). 4. As sample size increases, \( E(\bar{p}) \) approaches \( p \). ### Detailed Explanation **Understanding Graphs and Diagrams:** This section contains text boxes and radio buttons for inputting sample probabilities and selecting the correct statements. There are no visual graphs or complex diagrams, but the information looks to be organized in a Q&A format for educational purposes. **Instruction on Calculating Probabilities:** To solve for each part (a-d), one would typically standardize the sampling distribution of the sample portion \(\hat{p}\) using: \[ \hat{p} \text{ is approximately } N(p, \sqrt{\frac{p(1-p)}{n}}) \] - Calculate the standard error (SE) for each sample size \( n \). Using the Standard Normal Distribution (Z-distribution), center it around \[ Z = \frac{\hat{p} - p}{\sqrt{\frac{p (1 - p)}{n}}} \] By applying this standard, you can determine the values to plug in to calculate each probability and answer the sample size advantage question. This framework helps in understanding how sample size influences the precision of estimates in statistical analysis.
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