Hypothesis Testing, P-values and Inference: When Thinking like a Statistician Makes Sense

Instructor: Elaine Eisenbeisz
Product ID: 703552
  • Duration: 60 Min

recorded version

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Read Frequently Asked Questions

This webinar will explore the possibilities and limitations of research questions and hypothesis development. Attendees will learn the why and how of the scientific method and how to view the world with a statistician’s eyes.

Why Should You Attend:

Do you become tongue tied when explaining the meaning of a p-value? Would you like to know why the null hypothesis is so important to research? Why don’t studies prove anything? Are you pretty sure about what you want to say in plain English, but you’re not sure how to say it statistically?

This webinar will briefly review the history of the scientific method. It will explore the steps involved in developing a research question that can be tested with statistical hypotheses. Examples of research questions and hypotheses that can and cannot be tested will be presented. A brief lesson in statistical theory will explain why we don’t prove anything in research, we can only make really, really, good guesses providing we look at the problem the right way.

Areas Covered in the Webinar:

  • Brief history of the scientific method
  • Examples of when scientific methods are useful and when they are not.
  • 5 steps for hypothesis testing:
    1. Formulation of research questions and statistical hypotheses to explain and/or test phenomena.
    2. Specify the statistical hypotheses
    3. Choice of an appropriate test-statistic
    4. Compute probability and determine if results are significant
    5. Properly state conclusions and make inferences based on the test results
  • Suggestions for the best tests to use to address specific types of research, and how to structure the study research questions accordingly:
    • Tests of mean differences
    • Tests of correlation/association
  • Demonstration of formulation and testing hypotheses via SPSS statistical software. Covered tests will include t-tests, correlational analysis, multiple regression analysis

Who Will Benefit:

  • Researchers
  • Principal Investigators
  • Coordinators
  • Industry Sponsors
  • IRB members

Instructor Profile:

Elaine Eisenbeisz, is the owner and principal statistician of Omega Statistics, a private practice statistical consultancy based in Southern California. She has over 25 years of experience in creating data and information solutions for governmental agencies, large corporations, start-up companies and individual researchers. In addition to her technical expertise, Elaine possesses a talent for conveying statistical concepts and results in a way that people can intuitively understand.

Elaine's love of numbers began in elementary school where she placed in regional and statewide mathematics competitions. She attended University of California, Riverside, as a National Science Foundation Scholar, where she earned a B.S. in Statistics with a minor in Quantitative Management. Elaine completed her graduate studies with Texas A & M University, earning her post graduate certification in Applied Statistics. Her published and acknowledged works encompass many disciplines and include studies of U.S. Veteran populations, markers of autism, endodontic surgery techniques, and the relationship of between technology and civil unrest. Elaine is a member of The American Statistical Association as well as many other professional organizations. She is also a member of the Mensa High IQ Society. Client satisfaction is of utmost importance to Elaine and her company, Omega Statistics, holds an A+ rating with the Better Business Bureau.

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