Equivalence of Hypothesis Test and Confidence Interval Two different simple random samples are drawn from two different populations. The first sample consists of 20 people with 10 having a common attribute. The second sample consists of 2000 people with 1404 of them having the same common attribute. Compare the results from a hypothesis test of p1=p2 (with a 0.05 significance level) and a 95% confidence interval estimate of p1-p2
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
10. Hypothesis Testing for Two Samples
Two Proportions
Problem 8.4.23
Textbook Question
Constructing Confidence Intervals for p1-p2 You can construct a confidence interval for the difference between two population proportions p1-p2 by using the inequality below.
In Exercises 23–26, construct the indicated confidence interval for p1-p2. Assume the samples are random and independent.
Students Planning to Study Visual and Performing Arts In a survey of 10,000 students taking the SAT, 7% were planning to study visual and performing arts in college. In another survey of 8000 students taken 10 years before, 8% were planning to study visual and performing arts in college. Construct a 95% confidence interval for p1-p2, where p1 is the proportion from the recent survey and p2 is the proportion from the survey taken 10 years ago. (Adapted from College Board)

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Step 1: Identify the given values from the problem. From the recent survey, the sample size is n1 = 10,000 and the sample proportion is p̂1 = 0.07. From the survey taken 10 years ago, the sample size is n2 = 8,000 and the sample proportion is p̂2 = 0.08.
Step 2: Calculate the complement proportions for each sample. For the recent survey, q̂1 = 1 - p̂1 = 1 - 0.07. For the older survey, q̂2 = 1 - p̂2 = 1 - 0.08.
Step 3: Compute the standard error (SE) for the difference in proportions using the formula: SE = sqrt((p̂1 * q̂1 / n1) + (p̂2 * q̂2 / n2)). Substitute the values of p̂1, q̂1, n1, p̂2, q̂2, and n2 into the formula.
Step 4: Determine the critical value (z_c) for a 95% confidence level. For a 95% confidence interval, z_c is approximately 1.96.
Step 5: Construct the confidence interval for p1 - p2 using the formula: (p̂1 - p̂2) - z_c * SE < p1 - p2 < (p̂1 - p̂2) + z_c * SE. Substitute the values of p̂1, p̂2, z_c, and SE into the formula to find the interval.

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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. It is expressed with a certain level of confidence, such as 95%, indicating the probability that the interval includes the parameter. In the context of proportions, it helps estimate the difference between two population proportions based on sample data.
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Proportion and Sample Size
A proportion is a statistical measure that represents the fraction of a whole, often expressed as a percentage. In this case, p1 and p2 represent the proportions of students planning to study visual and performing arts from two different surveys. The sample size, denoted as n1 and n2, is crucial because larger samples generally provide more reliable estimates of the population proportions.
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Z-score and Standard Error
The Z-score is a statistical measurement that describes a value's relation to the mean of a group of values, expressed in terms of standard deviations. In constructing confidence intervals, the Z-score (z_c) is used to determine the margin of error. The standard error, calculated from the sample proportions and sizes, quantifies the variability of the sample proportion estimates, which is essential for determining the width of the confidence interval.
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