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DATA LITERACY: HOW TO MAKE EXPRMNTS ROBUST REPRODUCIBLE

Information Science

Computing & Information Technology

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B2B GT

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B2B & B2C

<P>This text 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 factors that make a study robust and reproducible. The book covers several major modules such as experimental design, data cleansing and preparation, statistical analysis, data management, and reporting. No specialized knowledge of statistics or computer programming is needed to fully understand the concepts presented.</P>

Data Literacy

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Duration

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Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Contents.
What is Data Literacy?.
Acknowledgments.
Why This Book?.
Designing Your Experiment.
1: Reproducibility and Robustness.
2: Choosing a Research Problem.
3: Basics of Data and Data Distributions.
4: Experimental Design: Measures, Validity, Sampling, Bias, Randomization, Power.
5: Experimental Design: Design Strategies and Controls.
6: Power Estimation.
Getting a &#x201C;Feel&#x201D; for Your Data.
7: The Data Cleansing and Analysis Pipeline.
8: Topics to Consider When Analyzing Data.
Statistics (Without Much Math!).
9: Null Hypothesis Statistical Testing and the t-Test.
10: The &#x201C;New Statistics&#x201D; and Bayesian Inference.
11: ANOVA.
12: Nonparametric Tests.
13: Correlation and Other Concepts You Should Know.
Make Your Data Go Farther.
14: How to Record and Report Your Experiments.
15: Data Sharing and Reuse.
16: The Revolution in Scientific Publishing.
Postscript: Beyond Data Literacy.
Index.

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Data Literacy