Data literacy : how to make your experiments robust and reproducible /

Data Literacy: How to Make Your Experiments Robust and Reproducible provides an overview of basic concepts and skills in handling data, which are common to diverse areas of science. Readers will get a good grasp of the steps involved in carrying out a scientific study and will understand some of the...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Smalheiser, Neil R. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam : Academic Press, 2017.
Temas:
Data mining.
Research > Data processing.
Medicine > Research > Data processing.
Data Mining
Research Design
Statistics
Exploration de donn�ees (Informatique)
M�edecine > Recherche > Informatique.
research (function)
REFERENCE > Questions & Answers.
Electronic books.
Statistics.
Statistiques.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Reproducibility and robustness
  • Choosing a research problem
  • Basics of data and data distribution
  • Experimental design: measures, validity, sampling, bias, randomization, power
  • Experimental design: design strategies and controls
  • Power estimation
  • The data cleansing and analysis pipeline
  • Topics to consider when analyzing data
  • Null hypothesis statistical testing and the t-Test
  • The "new statistics" and Bayesian inference
  • ANOVA
  • Non parametric tests
  • Correlation and other concepts you should know
  • How to record and report your experiments
  • Data sharing and reuse
  • The revolution in scientific publishing.

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