The Foundations of Inferential Statistics: Making Sense of the Statistics You Conduct
Once data has been collected, researchers then need to turn their data into usable information about their research questions. Descriptive statistics, like mean, median, or mode, can tell us only so much about our own observations/participants. Inferential statistics build off of descriptive statistics to help researchers make educated guesses about the world. Like any scientific tool, they can be immensely helpful when applied correctly, and they can be equally harmful when misused. This workshop provides an overview of null hypothesis significance testing (NHST), which is the most common category of inferential statistics. If you are used to running statistics that lead to p-values, then you are using NHST.
By the end of this workshop, you should be able to:
- Differentiate between descriptive and inferential statistics
- Understand what it means to test a null hypothesis and the conclusions you can draw about your data from adopting this approach
- Understand the limitations of the null hypothesis-testing approach
- Define the following concepts: p-values, statistical power, Type I and Type II errors, and confidence intervals
Audience: Faculty researchers seeking a refresher in the foundation of research design, graduate students or staff beginners
Format: A one-hour workshop will be delivered live via Zoom; a longer-form video will be available online after the live workshop has been delivered