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Ch. 9 - Inferences from Two Samples
Triola - Elementary Statistics 14th Edition
Triola14th EditionElementary StatisticsISBN: 9780137366446Not the one you use?Change textbook
Chapter 9, Problem 9.1.25a

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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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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Introduction to Confidence Intervals

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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Step 1: Write Hypotheses
Related Practice
Textbook Question

In Exercises 5–16, use the listed paired sample data, and assume that the samples are simple random samples and that the differences have a distribution that is approximately normal.


Do Men Talk Less than Women? Listed below are word counts of males and females in couple relationships (from Data Set 14 “Word Counts” in Appendix B).


a. Use a 0.05 significance level to test the claim that men talk less than women.


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Textbook Question

In Exercises 5–20, assume that the two samples are independent simple random samples selected from normally distributed populations, and do not assume that the population standard deviations are equal. (Note: Answers in Appendix D include technology answers based on Formula 9-1 along with “Table” answers based on Table A-3 with df equal to the smaller of n1-1 and n2-1)


Readability of Font On a Computer Screen The statistics shown below were obtained from a standard test of readability of fonts on a computer screen (based on data from “Reading on the Computer Screen: Does Font Type Have Effects on Web Text Readability?” by Ali et al., International Education Studies, Vol. 6, No. 3). Reading speed and accuracy were combined into a readability performance score (x), where a higher score represents better font readability.


a. Use a 0.05 significance level to test the claim that there is no significant difference in readability between Roman and Arial fonts.


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Textbook Question

In Exercises 5–20, assume that the two samples are independent simple random samples selected from normally distributed populations, and do not assume that the population standard deviations are equal. (Note: Answers in Appendix D include technology answers based on Formula 9-1 along with “Table” answers based on Table A-3 with df equal to the smaller of n1-1 and n2-1)


Magnet Treatment of Pain People spend around \$5 billion annually for the purchase of magnets used to treat a wide variety of pains. Researchers conducted a study to determine whether magnets are effective in treating back pain. Pain was measured using the visual analog scale, and the results given below are among the results obtained in the study (based on data from “Bipolar Permanent Magnets for the Treatment of Chronic Lower Back Pain: A Pilot Study,” by Collacott, Zimmerman, White, and Rindone, Journal of the American Medical Association, Vol. 283, No. 10). Higher scores correspond to greater pain levels.


a. Use a 0.05 significance level to test the claim that those treated with magnets have a greater mean reduction in pain than those given a sham treatment (similar to a placebo).


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Textbook Question

In Exercises 5–20, assume that the two samples are independent simple random samples selected from normally distributed populations, and do not assume that the population standard deviations are equal. (Note: Answers in Appendix D include technology answers based on Formula 9-1 along with “Table” answers based on Table A-3 with df equal to the smaller of n1-1 and n2-1)


Better Tips by Giving Candy An experiment was conducted to determine whether giving candy to dining parties resulted in greater tips. The mean tip percentages and standard deviations are given below along with the sample sizes (based on data from “Sweetening the Till: The Use of Candy to Increase Restaurant Tipping,” by Strohmetz et al., Journal of Applied Social Psychology, Vol. 32, No. 2).


a. Use a 0.05 significance level to test the claim that giving candy does result in greater tips.

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Textbook Question

In Exercises 5–16, use the listed paired sample data, and assume that the samples are simple random samples and that the differences have a distribution that is approximately normal.


Heights of Presidents A popular theory is that presidential candidates have an advantage if they are taller than their main opponents. Listed are heights (cm) of presidents along with the heights of their main opponents (from Data Set 22 “Presidents” in Appendix B).


a. Use the sample data with a 0.05 significance level to test the claim that for the population of heights of presidents and their main opponents, the differences have a mean greater than 0 cm.


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Textbook Question

Cigarette Pack Warnings A study was conducted to find the effects of cigarette pack warnings that consisted of text or pictures. Among 1078 smokers given cigarette packs with text warnings, 366 tried to quit smoking. Among 1071 smokers given cigarette packs with warning pictures, 428 tried to quit smoking. (Results are based on data from “Effect of Pictorial Cigarette Pack Warnings on Changes in Smoking Behavior,” by Brewer et al., Journal of the American Medical Association.) Use a 0.01 significance level to test the claim that the proportion of smokers who tried to quit in the text warning group is less than the proportion in the picture warning group.


b. Test the claim by constructing an appropriate confidence interval.

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