Experimental Design
A well-designed experiment should provide robust and unbiased results. Scientific rigor, as defined by the NIH, is the strict application of the scientific method to ensure robust and unbiased experimental design, methodology, analysis, interpretation and reporting of results.
These resources are to aid in the planning of a rigorous experimental design and are not meant to replace or provide rules as to how experiments should be conducted. They are merely guidance to aid in the experimental planning process. More resources on reproducibility are available on the reproducibility by topic webpage.
In January 2014, NIH launched a series of initiatives to enhance rigor and reproducibility in research. As a part of this initiative, NIGMS, along with nine other NIH institutes and centers, issued the funding opportunity announcement RFA-GM-15-006 to develop, pilot and disseminate training modules to enhance data reproducibility (including experimental design). Graduate students, postdoctoral fellows and early stage investigators are the primary audiences for these training modules. [Text taken from NIH website]
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Sample size and power estimate online calculator. Free, Online, Easy-to-Use Power and Sample Size Calculators
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Experimental Design Checklist (based upon Drs. Casadevall and Fang's How-To Guide)
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Rigorous Clinical Trials Resources by the NIH’s Office of Disease Prevention (ODP)
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Rigorous Science: A How-To Guide by Arturo Casadevall and Ferric Fang
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Experimental Design Learning Module from Indiana University School of Medicine
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Experimental Design Assistant for Planning Animal Experiments from NC3Rs
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Experimental Design and Statistics from NC3Rs
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Let's Experiment: A Guide for Scientists Working at the Bench from iBiology- Scientists from a variety of backgrounds give concrete steps and advice to help you build a framework for how to design experiments in biological research.
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Beyond Rigor: Appropriate Analysis by Patricia Campbell and Eric Jolly
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Issues with data and analyses: Errors, underlying themes, and potential solutions Andrew W. Brown, Kathryn A. Kaiser, and David B. Allison
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Scientific Method: Statistical Errors by Regina Nuzzo
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Analysis/Reporting Learning Module from Indiana University School of Medicine
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Understanding Statistics and Experimental Design: How to Not Lie with Statistics by Michael H. Herzog, Gregory Francis, and Aaron Clarke
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Blind analysis: Hide results to seek the truth by Robert MacCoun and Saul Perlmutter (blinding strategies for empirical research)
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Identifying and Avoiding Bias in Research (Clinical) by Christopher J. Pannucci and Edwin G. Wilkins
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How Scientists Fool Themselves - and How They Can Stop by Regina Nuzzo
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Strategies for Addressing a Broader Definition of Conflicts of Interest by Ross J. McKinney Jr. and Heather Pierce
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Reproducibility of Experiments in Biomedical Research by Shai Silberberg - YouTube video on unconscious and publication bias
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9 Types of Research Bias and How to Avoid Them (qualitative research)
