Hypothesis testing and p-value pitfalls
Hypothesis testing is the foundation of statistical inference procedures. Yet the meaning of the results (e.g., p-values and confidence intervals) is often not fully understood or appreciated by investigators, potentially leading to a misinterpretation of the results.
In this seminar, we will review the history and intent of inferential testing, discuss what Type I and II errors are, and explore how they impact interpretation of results and are related to statistical power. We will examine current criticisms of the use and interpretations of results based on p-values and provide recommendations for addressing limitations of the classic null hypothesis significance testing framework.
Participants in this seminar will gain a more in-depth understanding of the objectives and limitations of classical inferential testing, which will allow them to more critically examine and interpret statistical analysis results.
• Be able to formulate research hypotheses for a statistical test
• Understand p-values and confidence intervals
• Appreciate Type I and II errors and their relation to power analysis and sample size calculations
Center for Health and Technology, Room 1347, 4610 X Street, Sacramento
Registration preferred but not required. Register here: https://bit.ly/2X0drCy
For more information contact: Sandra Taylor, Ph.D., at firstname.lastname@example.org
This seminar series is supported by the following NIH-funded centers: CTSC, MIND IDDRC, Cancer Center and the Environmental Health Sciences Center.