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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 9.1.25c

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.”


c. Use a 0.05 significance level to test the claim that the two population proportions are equal. What do you conclude?

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Step 1: Define the null and alternative hypotheses. The null hypothesis (H₀) states that the two population proportions are equal: p₁ = p₂. The alternative hypothesis (H₁) states that the two population proportions are not equal: p₁ ≠ p₂.
Step 2: Calculate the sample proportions for each group. For the first sample, the proportion is p̂₁ = 112 / 200. For the second sample, the proportion is p̂₂ = 88 / 200.
Step 3: Compute the pooled proportion (p̂) using the formula: p̂ = (x₁ + x₂) / (n₁ + n₂), where x₁ and x₂ are the number of successes in each sample, and n₁ and n₂ are the sample sizes.
Step 4: Calculate the standard error (SE) for the difference in proportions using the formula: SE = √[p̂(1 - p̂)(1/n₁ + 1/n₂)].
Step 5: Compute the test statistic (z) using the formula: z = (p̂₁ - p̂₂) / SE. Compare the test statistic to the critical value for a two-tailed test at the 0.05 significance level (±1.96). If |z| > 1.96, reject the null hypothesis; otherwise, fail to reject the null hypothesis.

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Key Concepts

Here are the essential concepts you must grasp in order to answer the question correctly.

Confidence Intervals

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% or 99%. It provides an estimate of uncertainty around a sample statistic, such as a mean or proportion. In the context of comparing two population proportions, overlapping confidence intervals can suggest that the proportions may not be significantly different.
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Introduction to Confidence Intervals

Hypothesis Testing

Hypothesis testing is a statistical method used to make inferences about population parameters based on sample data. It involves formulating a null hypothesis (e.g., the two population proportions are equal) and an alternative hypothesis (e.g., the proportions are not equal). A significance level, often set at 0.05, is used to determine whether to reject the null hypothesis based on the calculated test statistic and p-value.
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Guided course
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Step 1: Write Hypotheses

Proportions and Sample Size

Proportions represent the fraction of a sample that possesses a certain attribute, calculated as the number of successes divided by the total sample size. In this case, the proportions of individuals with the attribute in each sample are 112/200 and 88/200. The sample size affects the precision of the estimates and the power of the hypothesis test, with larger samples generally providing more reliable results.
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Sampling Distribution of Sample Proportion
Related Practice
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.


c. Does it appear that magnets are effective in treating back pain? Is it valid to argue that magnets might appear to be effective if the sample sizes are larger?


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Textbook Question

Can Dogs Detect Malaria? A study was conducted to determine whether dogs could detect malaria from socks worn by malaria patients and socks worn by patients without malaria. Among 175 socks worn by malaria patients, the dogs made correct identifications 123 times. Among 145 socks worn by patients without malaria, the dogs made correct identifications 131 times (based on data presented at an annual meeting of the American Society of Tropical Medicine, by principal investigator Steve Lindsay). Use a 0.05 significance level to test the claim of no difference between the two rates of correct responses.


c. What do the results suggest about the use of dogs to detect malaria?

Textbook Question

Independent Samples Which of the following involve independent samples?


c. Data Set 1 “Body Data” includes a sample of pulse rates of 147 women and a sample of pulse rates of 153 men.

Textbook Question

Clinical Trials of OxyContin OxyContin (oxycodone) is a drug used to treat pain, but it is well known for its addictiveness and danger. In a clinical trial, among subjects treated with OxyContin, 52 developed nausea and 175 did not develop nausea. Among other subjects given placebos, 5 developed nausea and 40 did not develop nausea (based on data from Purdue Pharma L.P.). Use a 0.05 significance level to test for a difference between the rates of nausea for those treated with OxyContin and those given a placebo.


c. Does nausea appear to be an adverse reaction resulting from OxyContin?

Textbook Question

Confidence Interval Assume that we want to use the sample data in Exercise 1 for constructing a confidence interval to be used for testing the given claim.


c. If the resulting confidence interval is -5.8 admissions <ud < -0.9 admissions, what do you conclude?

Textbook Question

F Test Statistic


c. If testing the claim that sigma2,1 is not equals to sigma2,2 what do we know about the two samples if the test statistic F is very close to 1?