Questions and Answers
Why is it important to make clear how results are achieved in data analysis?
What may happen if activities involved in reproducibility only occur at the end of an analysis?
Which of the following is a potential consequence of assuming an incorrect distribution in data analysis?
What is one potential issue if resources have been moved on to other projects before challenges to the data analysis are addressed?
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What may occur if incorrect assumptions about distributions are made in data analysis?
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How does ensuring transparency in the data analysis process impact the challenge of incorrect analysis?
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What is required for reproducibility according to the text?
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Which programming tool in the R environment allows for achieving full documented code?
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What is literate statistical programming according to Knuth, 1992?
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What does full documentation for reproducibility include?
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What is the key difference between validating results through replication and validation through reproduction as per the text?
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Which tool is recommended in the text for producing documents with code explanations?
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Why is it important to set the random seed in replication when randomness is present in statistical or machine learning techniques?
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What does setting the random seed accomplish in simulation studies?
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How can 'doing things by hand' potentially affect reproducing work in data analysis?
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In a simulation study, why does each simulation need to be based on a series of pseudo-random numbers?
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What is the purpose of setting the random seed in statistical or machine learning techniques involving randomness?
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Why is it necessary for two simulations to be based on the same series of pseudo-random numbers to obtain identical results?
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