Are Nuclear Plants Safe? In a Gallup poll of 1038 adults, 47% said that nuclear plants are safe, 49% said that they are not safe, and 4% had no opinion. Construct a 95% confidence interval estimate of the proportion of all adults who say that nuclear plants are safe. Based on the result, can we conclude that fewer than half of all adults say that nuclear plants are safe?
Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 53m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample2h 19m
- 10. Hypothesis Testing for Two Samples3h 22m
- 11. Correlation1h 6m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
8. Sampling Distributions & Confidence Intervals: Proportion
Confidence Intervals for Population Proportion
Problem 9.1.25a
Textbook Question
Overlap of Confidence Intervals In the article “On Judging the Significance of Differences by Examining the Overlap Between Confidence Intervals,” by Schenker and Gentleman (American Statistician, Vol. 55, No. 3), the authors consider sample data in this statement: “Independent simple random samples, each of size 200, have been drawn, and 112 people in the first sample have the attribute, whereas 88 people in the second sample have the attribute.”
a. Use the methods of this section to construct a 95% confidence interval estimate of the difference p1-p2. What does the result suggest about the equality of p1 and p2
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1
Step 1: Identify the problem and the given data. We are tasked with constructing a 95% confidence interval for the difference between two population proportions (p1 - p2). The given data includes two independent random samples: Sample 1 has n1 = 200 and x1 = 112, while Sample 2 has n2 = 200 and x2 = 88.
Step 2: Calculate the sample proportions for each group. The sample proportion for the first group is p̂1 = x1 / n1, and for the second group, it is p̂2 = x2 / n2. These proportions represent the observed frequencies of the attribute in each sample.
Step 3: Compute the standard error (SE) for the difference between the two proportions. The formula for the standard error is: SE = sqrt((p̂1 * (1 - p̂1) / n1) + (p̂2 * (1 - p̂2) / n2)). This accounts for the variability in both sample proportions.
Step 4: Determine the critical value for a 95% confidence level. For a two-tailed test, the critical value corresponds to the z-score associated with 95% confidence, which is approximately 1.96. This value will be used to calculate the margin of error.
Step 5: Construct the confidence interval for the difference (p1 - p2). The formula is: (p̂1 - p̂2) ± (z * SE), where z is the critical value and SE is the standard error. Interpret the interval to assess whether it includes 0, which would suggest equality between p1 and p2.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Confidence Interval
A confidence interval is a range of values, derived from sample statistics, that is likely to contain the true population parameter with a specified level of confidence, typically 95%. It provides an estimate of uncertainty around a sample statistic, allowing researchers to infer about the population. For example, if a 95% confidence interval for the difference in proportions is calculated, it suggests that if the same sampling method were repeated, 95% of the intervals would contain the true difference.
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Difference in Proportions
The difference in proportions refers to the comparison of two population proportions, denoted as p1 and p2. In the context of the question, it involves calculating the difference between the proportion of individuals with a certain attribute in two independent samples. This difference helps in assessing whether there is a statistically significant disparity between the two groups, which can be crucial for hypothesis testing.
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Difference in Proportions: Hypothesis Tests
Hypothesis Testing
Hypothesis testing is a statistical method used to make inferences about population parameters based on sample data. In this context, it involves testing the null hypothesis that p1 equals p2 against the alternative hypothesis that they are not equal. The results from the confidence interval can indicate whether the null hypothesis can be rejected, thus providing insights into the equality or difference of the two proportions.
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