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Ch. 9 - Inferences from Two Samples
Triola - Elementary Statistics 14th Edition
Triola14th EditionElementary StatisticsISBN: 9780137366446당신이 사용하는 게 아니라요?교과서 변경
모든 교과서Triola 14th EditionCh. 9 - Inferences from Two Samples문제 9.5.4
9장, 문제 9.5.4

Randomization vs t Test Two samples of commute times from Boston and New York are randomly selected and it is found that the samples sizes are n1 = 18 and n2 = 12 and each of the two samples appears to be from a population with a distribution that is dramatically far from normal. Which method is more likely to yield better results for testing Mu1 is not equals to Mu2. Hypothesis test using the t distribution (as in Section 9-2) or the resampling method?

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Step 1: Understand the problem. The goal is to determine which method is more appropriate for testing the hypothesis that the population means (μ₁ and μ₂) of commute times for Boston and New York are not equal, given that the sample sizes are n₁ = 18 and n₂ = 12, and the population distributions are far from normal.
Step 2: Recall the assumptions of the t-test. The t-test assumes that the data comes from a normal distribution or that the sample sizes are sufficiently large for the Central Limit Theorem to apply. Since the population distributions are dramatically far from normal and the sample sizes are relatively small (n₁ = 18 and n₂ = 12), the t-test may not yield reliable results.
Step 3: Consider the resampling method. Resampling methods, such as bootstrapping or permutation tests, do not rely on the assumption of normality. These methods involve repeatedly sampling from the observed data to create a sampling distribution, making them more robust for non-normal data.
Step 4: Compare the two methods. Given the small sample sizes and the non-normality of the population distributions, the resampling method is likely to yield better results because it does not depend on the normality assumption and can handle small sample sizes effectively.
Step 5: Conclude the reasoning. Based on the information provided, the resampling method is more appropriate for testing the hypothesis that μ₁ ≠ μ₂ under these conditions. The t-test is less suitable due to the violation of its assumptions.

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주요 개념

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Randomization

Randomization is a fundamental principle in statistics that involves randomly assigning subjects to different groups to eliminate bias and ensure that the groups are comparable. This method helps in making valid inferences about the population from which the samples are drawn. In the context of hypothesis testing, randomization can enhance the reliability of results, especially when dealing with non-normal distributions.
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07:09
Intro to Random Variables & Probability Distributions

t Test

The t Test is a statistical method used to determine if there is a significant difference between the means of two groups. It assumes that the data is normally distributed and is particularly useful when sample sizes are small. However, when the underlying population distributions are not normal, the t Test may yield unreliable results, making it essential to consider alternative methods.
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Step 2: Calculate Test Statistic

Resampling Method

The resampling method, including techniques like bootstrapping, involves repeatedly drawing samples from the observed data to estimate the sampling distribution of a statistic. This approach is particularly useful when the assumptions of traditional parametric tests, such as normality, are violated. Resampling can provide more accurate confidence intervals and hypothesis tests, especially in cases with small sample sizes or non-normal distributions.
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03:26
Calculating the Median
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교과서 질문

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

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교과서 질문

Test for Normality For the hypothesis test described in Exercise 2, the sample sizes are n1 = 2208 and n2 = 1986 When using the F test with these data, is it correct to reason that there is no need to check for normality because both samples have sizes that are greater than 30?

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교과서 질문

Robust What does it mean when we say that the F test described in this section is not robust against departures from normality?

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교과서 질문

No Variation in a Sample An experiment was conducted to test the effects of alcohol. Researchers measured the breath alcohol levels for a treatment group of people who drank ethanol and another group given a placebo. The results are given below (based on data from “Effects of Alcohol Intoxication on Risk Taking, Strategy, and Error Rate in Visuomotor Performance,” by Streufert et al., Journal of Applied Psychology, Vol. 77, No. 4). Use a 0.05 significance level to test the claim that the two sample groups come from populations with the same mean.


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교과서 질문

Degrees of Freedom In Exercise 20 “Blanking Out on Tests,” using the “smaller of n1-1 and n2-1” for the number of degrees of freedom results in df=15 Find the number of degrees of freedom using Formula 9-1. In general, how are hypothesis tests and confidence intervals affected by using Formula 9-1 instead of the “smaller of n1-1 and n2-1 ”?

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교과서 질문

Color and Creativity Researchers from the University of British Columbia conducted trials to investigate the effects of color on creativity. Subjects with a red background were asked to think of creative uses for a brick; other subjects with a blue background were given the same task. Responses were scored by a panel of judges and results from scores of creativity are given below. Use a 0.05 significance level to test the claim that creative task scores have the same variation with a red background and a blue background.

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