Statistical inference using hypothesis testing is ubiquitous in science. Several misconceptions and misinterpretations of p-values have arisen over the years, which can lead to challenges communicating the correct interpretation of results.
In this training, we will:
- Review the p-value definition
- Examine common misconceptions and misinterpretations of p-values
- Discuss the recommendation to stop using the phrase ‘statistically significant’
- Discuss recommendations for moving forward
This training is appropriate for those with some experience conducting statistical inference using hypothesis testing.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
About the Instructor
Julia Sharp is faculty in the Department of Statistics at Colorado State University where she is the Director of the Graybill Statistics & Data Science Laboratory. She is also the owner of Sharp Analytics LLC, where she is the lead statistical collaborator. She earned her M.S. and Ph.D. in Statistics from Montana State University.
Julia has experience collaborating with researchers in many domains, using her expertise in applied statistics to inform and advance scientific research. Her statistical toolbox is broad and includes study design, analysis of a wide-range of data types (e.g., repeated observations over time, categorical data), and knowledge of popular statistical computing software.
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