Data literacy : how to make your experiments robust and reproducible / Neil R. Smalheiser.

Format:

Book

Author/Creator:

Smalheiser, Neil R., author.

Contributor:

ProQuest ebook central.

Language:

English

Subjects (All):

Research--Data processing.

Research.

Experimental design.

Data mining.

Medicine--Research--Data processing.

Medicine.

Medicine--Research.

Physical Description:

1 online resource

Place of Publication:

London ; San Diego, CA : Academic Press, [2017]

System Details:

text file

Contents:

Front Cover; DATA LITERACY; DATA LITERACY: HOW TO MAKE YOUR EXPERIMENTS ROBUST AND REPRODUCIBLE; Copyright; Contents; What Is Data Literacy?; Acknowledgments; Why This Book?; References; A

DESIGNING YOUR EXPERIMENT; 1

Reproducibility and Robustness; BASIC TERMS AND CONCEPTS; Replicating the Data; Replicating the Methods; Replicating the Findings; Robustness and Generalizability; Replicating the Conclusion; REPRODUCIBILITY AND ROBUSTNESS TALES TO GIVE YOU NIGHTMARES; Cell Culture Experiments; Animal Behavioral Experiments; THE WAY FORWARD; References; 2

Choosing a Research Problem

INTRODUCTIONSCIENTIFIC STYLES AND MIND-SETS; PROGRAMMATIC SCIENCE VERSUS LILY-PAD SCIENCE; CRITERIA (AND MYTHS) IN CHOOSING A RESEARCH PROBLEM; Myths; Should a Research Question Be Logical?; Should You Avoid a Research Question That Directly Contradicts Reliable Experimental Findings?; STRONG INFERENCE; DESIGNING STUDIES AS COURT TRIALS; What the Sixth Sense Teaches Us About Scientific Evidence and Persuasion [20]; "Degrees of Freedom"; What to Do When No Strong Hypothesis Is Apparent?; Should a Good Experiment Produce Findings That Remain "True" Over Time?; References

3

Basics of Data and Data DistributionsINTRODUCTION; AVERAGES; VARIABILITY; THE BELL-SHAPED ("NORMAL") CURVE; NORMALIZATION; DISTRIBUTION SHAPE; A PEEK AHEAD AT SAMPLING, EFFECT SIZES, AND STATISTICAL SIGNIFICANCE; Statistical Testing: Measuring the Variability Associated With Sampling; Statistical Testing: Comparing the Means of Two Groups Using the t-Test; OTHER IMPORTANT CURVES AND DISTRIBUTIONS; PROBABILITIES THAT INVOLVE DISCRETE COUNTS; CONDITIONAL PROBABILITIES; Taking an HIV Test; ARE MOST PUBLISHED SCIENTIFIC FINDINGS FALSE?; Reference

4

Experimental Design: Measures, Validity, Sampling, Bias, Randomization, PowerMEASURES; Only What Gets Coded Gets Noticed; Different Ways of Measuring Can Lead to Different Interpretations of the Same Data; Primary Versus Inferred Measures; Derived Measures; VALIDITY; Errors and Variation; SAMPLING AND RANDOMIZATION; How to Randomize; And When NOT to Randomize; SOURCES OF BIAS IN EXPERIMENTS; POWER ESTIMATION; References; 5

Experimental Design: Design Strategies and Controls; "A FEELING FOR THE ORGANISM"; BUILDING AN EXPERIMENTAL SERIES IN LAYERS; First Layer; Second Layer; Third Layer

Fourth LayerSPECIFIC DESIGN STRATEGIES; Paired Versus Unpaired Designs; Stratification, Balancing, and Blocking Designs; Factorial and Nested Designs; Studying Large Versus Small Effects; CONTROLS; Positive Controls; Assumption Controls; Intersystem Controls; Negative Controls; SPECIFIC, NONSPECIFIC, AND BACKGROUND EFFECTS; SIMPLE VERSUS COMPLEX EXPERIMENTAL DESIGNS; HOW MANY TIMES SHOULD ONE REPEAT AN EXPERIMENT BEFORE PUBLISHING?; SOME COMMON PITFALLS TO AVOID; Caveat: Primary Versus Secondary Findings in Scientific Papers; Caveat: Commercial Proprietary Kits and Reagents

Notes:

Electronic reproduction. Ann Arbor, MI Available via World Wide Web.

Description based on online resource; title from digital title page (viewed on November 01, 2017).

Other Format:

Print version :

ISBN:

9780128113073

0128113073

Publisher Number:

99974340733

Access Restriction:

Restricted for use by site license.