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Statistics and Research Methods for Acute Care and General SurgeonsIntroduction to Statistical Method

Statistics and Research Methods for Acute Care and General Surgeons: Introduction to Statistical... [A scientific hypothesis is the initial building block in the scientific method. The basic idea of a hypothesis is that there is no pre-determined outcome. Thus, when a study or a protocol is designed, the hypothesis is your “best guess” about the effect of a treatment based on biological plausibility and previous literature results. Hypothesis testing requires the construction of a statistical model, meaning to test whether the data of an experiment follow a chance or casual processes or are truly responsible for the results obtained.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Statistics and Research Methods for Acute Care and General SurgeonsIntroduction to Statistical Method

Part of the Hot Topics in Acute Care Surgery and Trauma Book Series
Editors: Ceresoli, Marco; Abu-Zidan, Fikri M.; Staudenmayer, Kristan L.; Catena, Fausto; Coccolini, Federico
Gianotti, Luca
Statistics and Research Methods for Acute Care and General Surgeons — Dec 14, 2022
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Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
ISBN
978-3-031-13817-1
Pages
41 –54
DOI
10.1007/978-3-031-13818-8_4
Publisher site
See Chapter on Publisher Site

Abstract

[A scientific hypothesis is the initial building block in the scientific method. The basic idea of a hypothesis is that there is no pre-determined outcome. Thus, when a study or a protocol is designed, the hypothesis is your “best guess” about the effect of a treatment based on biological plausibility and previous literature results. Hypothesis testing requires the construction of a statistical model, meaning to test whether the data of an experiment follow a chance or casual processes or are truly responsible for the results obtained.]

Published: Dec 14, 2022

Keywords: Hypothesis; Aim; Type I error; Type II error; P value; Bias; Biostatistics

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