What Are We Testing? Refer to the sample data in Exercise 1. Assuming that we use the Wilcoxon rank-sum test with those data, identify the null hypothesis and all possible alternative hypotheses.
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
9. Hypothesis Testing for One Sample
Steps in Hypothesis Testing
Problem 13.5.5
Textbook Question
Using the Kruskal-Wallis Test
In Exercises 5–8, use the Kruskal-Wallis test.
HIC Measurements Use the sample data from Exercise 1 with a 0.05 significance level to test the claim that small, midsize, large, and SUV vehicles have the same median HIC measurement in car crash tests.

1
Step 1: Understand the Kruskal-Wallis test. It is a non-parametric test used to compare the medians of three or more independent groups. It does not assume normality and is suitable for ordinal or continuous data.
Step 2: Organize the data. Combine all the HIC measurements from the small, midsize, large, and SUV vehicle groups into a single dataset. Rank all the values from smallest to largest, assigning tied ranks if necessary.
Step 3: Calculate the sum of ranks for each group. For each vehicle type (small, midsize, large, SUV), sum the ranks of the HIC measurements within that group.
Step 4: Compute the Kruskal-Wallis test statistic using the formula: , where N is the total number of observations, Ri is the sum of ranks for group i, and ni is the number of observations in group i.
Step 5: Compare the test statistic (H) to the critical value from the chi-square distribution table with degrees of freedom equal to the number of groups minus 1. If H exceeds the critical value, reject the null hypothesis that the medians are the same across all groups.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Kruskal-Wallis Test
The Kruskal-Wallis test is a non-parametric statistical method used to determine if there are statistically significant differences between the medians of three or more independent groups. It is an extension of the Mann-Whitney U test and is particularly useful when the assumptions of ANOVA (such as normality) are not met. The test ranks all data points from all groups together and compares the sum of ranks between groups.
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Step 2: Calculate Test Statistic
Median
The median is a measure of central tendency that represents the middle value of a dataset when it is ordered from least to greatest. Unlike the mean, the median is less affected by outliers and skewed data, making it a robust measure for understanding the central location of a dataset. In the context of the Kruskal-Wallis test, the median is used to assess whether different groups have similar central values.
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Calculating the Median
Significance Level
The significance level, often denoted as alpha (α), is the threshold used to determine whether to reject the null hypothesis in hypothesis testing. A common significance level is 0.05, which indicates a 5% risk of concluding that a difference exists when there is none. In the context of the Kruskal-Wallis test, if the p-value is less than 0.05, it suggests that at least one group has a different median HIC measurement, leading to the rejection of the null hypothesis.
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Step 4: State Conclusion Example 4
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