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Experimental Design webinar: Learning From Our GWAS Mistakes

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  • Experimental Design webinar: Learning From Our GWAS Mistakes

    Abstract:

    Why is bad or non-existent experimental design so prevalent in our field?

    In the April 2012 issue of Biostatistics1, Dr. Christophe Lambert co-authored an invited editorial that examines this very question with genome-wide association studies (GWAS) as context. Though GWAS is an easy target, echoes of the same mistakes go on in every corner of biological research, including gene expression and next-gen sequencing!

    In this webcast, Dr. Lambert expounds upon the deep systemic problems that plague our field stepping back to look at them from a broader paradigmatic perspective. He discusses real examples of poorly designed experiments showing what should not be done and concludes with practical advice on how to avoid common and costly mistakes in your own research.


    By attending this webcast you will:

    Gain a greater appreciation of the scientific method
    Understand the importance of experimental design
    Learn how to better design and analyze your next experiment
    Realize how we may be fooling ourselves into thinking current NGS bioinformatic filtering have located causative variants


    Additional topics include:

    Why experimental design is so often ignored or botched
    Hypothesis generation, falsification, ceteris paribus and the challenge of applying the scientific method to aggregated complex systems
    Moving research from correlation to causation
    How applying the scientific method and learning through our mistakes is in conflict with the implicit metrics of academia and perhaps human nature


    Join us and you're sure to walk away with actionable information that will help you approach your research with new perspectives. Register at: www2.gotomeeting.com/register/813496074


    http://www.goldenhelix.com/Events/re...kes/index.html

  • #2
    I forgot to mention that, of course, this webinar is free for anyone to attend!

    Comment

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