Hypothesis Tests and P-Values

DescriptionA one-hour seminar on null hypothesis significance testing and understanding the use and interpretation of p-values.

Link: Seminar recording

Who created this resourceThis seminar was given by Sandra Taylor, PhD, Principal Statistician at the University of California-Davis’ Biostatistics, Epidemiology and Research Design Program.

What does this resource offer
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. This seminar provides an understanding of the objectives and limitations of classical inferential testing, thereby supporting a more critical examination and interpretation of statistical analysis results. Specifically, this seminar: 

  • reviews the history and intent of inferential testing, 
  • discusses what Type I and II errors are, how to interpret results of a statistical test in light of these errors, and how they relate to the power of a statistical test,
  • illustrates p-values and confidence intervals with visual simulations,
  • explains concerns about multiple testing and how to address them, and
  • provides guidance on how to report results of statistical tests.

Finally, the seminar examines current criticisms of the use and interpretations of results based on p-values and provides recommendations for addressing the limitations of the classic null hypothesis significance testing framework.