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Hypothesis Testing, P-values and Inference: When Thinking like a Statistician Makes Sense 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: o Tests of mean differences o 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

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