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Answer: The data contain outliers.
The bootstrap technique is a resampling method used for estimating statistics on a population by sampling a dataset with replacement. It is generally robust to the shape of the underlying distribution of the data, which means that it can handle non-normal distributions and asymmetry, making options A and D incorrect. The bootstrap method requires the data to be independent, which contradicts option B, making it incorrect as well. However, the presence of outliers can significantly affect the bootstrap estimates, as they can skew the resampled distributions and lead to biased results. Therefore, the correct answer is C, as outliers can render the bootstrap technique ineffective.
Author: LeetQuiz Editorial Team
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In a risk management consulting firm, a team responsible for statistical modeling is evaluating the appropriateness of using the bootstrap method to analyze a particular data sample. The bootstrap method is a resampling technique used to estimate statistics on a dataset by sampling with replacement. This technique can be very effective for assessing the accuracy of sample estimates. However, under what circumstances would the use of the bootstrap method be considered ineffective or inappropriate for the data sample analysis?
A
The data follow an asymmetric distribution.
B
The data are independent and identically distributed.
C
The data contain outliers.
D
The data are normally distributed.
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