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 your 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 scientific method. We 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. We will also discuss three ways of interpretation that in combination can be used for better decision making, namely, p-values, effect sizes, and confidence intervals.
Areas Covered in the Webinar:
Brief history of the scientific method
Examples of when scientific methods is useful, and when it is not are not.
5 steps for hypothesis testing:
Formulation of research questions and statistical hypotheses to explain and/or test phenomena.
Specify the statistical hypotheses
Choice of an appropriate test-statistic
Compute probability and determine if results are significant
Properly state conclusions and make inferences based on the test results
Why p-values are not enough. A review of effect sized and confidence intervals.
Suggestions for the best tests to use to address specific types research, and how to structure the study research questions accordingly:
Tests of mean differences
Tests of correlation/association
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 your 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 scientific method. We 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. We will also discuss three ways of interpretation that in combination can be used for better decision making, namely, p-values, effect sizes, and confidence intervals.
Areas Covered in the Webinar:
Brief history of the scientific method
Examples of when scientific methods is useful, and when it is not are not.
5 steps for hypothesis testing:
Formulation of research questions and statistical hypotheses to explain and/or test phenomena.
Specify the statistical hypotheses
Choice of an appropriate test-statistic
Compute probability and determine if results are significant
Properly state conclusions and make inferences based on the test results
Why p-values are not enough. A review of effect sized and confidence intervals.
Suggestions for the best tests to use to address specific types research, and how to structure the study research questions accordingly:
Tests of mean differences
Tests of correlation/association
Speakers
Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California. Elaine earned her B.S. in Statistics at UC Riverside and received her Master’s Certification in Applied Statistics from Texas A&M.Elaine is a member in good standing with the American Statistical Association and a member of the Mensa High IQ Society. Omega Statistics holds an A+ rating with the Better Business Bureau.
Elaine has designed the methodology and analyzes data for numerous studies in the clinical, biotech, and health care fields. Elaine has also works as a contract statistician with private researchers and biotech start-ups as well as with larger companies such as Allergan, Nutrisystem and Rio Tinto Minerals. Throughout her tenure as a private practice statistician, she has published work with researchers and colleagues in peer-reviewed journals.
