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Validity, reliability, and significance: empirical methods for NLP and data science

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Preface -- Acknowledgments -- Introduction -- Validity -- Reliability -- Significance -- Worked-Through Example: Analyzing Inferential Reproducibility -- Bibliography.

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Bibliographic Details
Main Authors: Riezler, Stefan (Author) , Hagmann, Michael (Author)
Format: Book/Monograph
Language:English
Published: Cham Springer Nature Switzerland 2024
Cham Imprint: Springer 2024
Edition:2nd ed. 2024
Series:Synthesis Lectures on Human Language Technologies
DOI:10.1007/978-3-031-57065-0
Online Access:Resolving-System, lizenzpflichtig: https://doi.org/10.1007/978-3-031-57065-0
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Author Notes:by Stefan Riezler, Michael Hagmann
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Validity, reliability, and significance: empirical methods for NLP and data science by Riezler, Stefan (Author) , Hagmann, Michael (Author) ,

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Validity, reliability, and significance: empirical methods for NLP and data science by Riezler, Stefan (Author) , Hagmann, Michael (Author) ,


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Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science by Riezler, Stefan (Author)


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