Summary and Setup
This is a new lesson built with The Carpentries Workbench.
Authors
Sarah Kaspar
Carolyn Mukiri Kambona
Summary
At the end of this course, you will be able to:
- comprehend the concept of multiple testing and its implications, including the increased likelihood of observing low p-values by chance.
- be able to differentiate between different types of errors, (false positives and false negatives) and understand their significance in various research contexts.
- understand the concept of family-wise error rate (FWER) multiple testing scenarios and grasp methods like the Bonferroni procedure for controlling it.
- gain proficiency in interpreting adjusted p-values resulting from FWER correction and applying them in statistical analyses like ANOVA and post-hoc tests.
- appreciate the challenges posed by high-throughput data analysis and understand the importance of controlling FDR using methods like the Benjamini-Hochberg procedure.
- apply the concepts learned to real-world scenarios, such as analyzing RNA-Seq data by selecting appropriate error rates and methods based on their research questions.
Tools Required
- Statistical software R (download)
- Basic knowledge of programming in R is beneficial for understanding the coding examples.
Data Sets
For all example data in this lesson, links for downloading them are provided during the episodes, which will allow you to follow along with coding.
Preparation
For the coding exercises, you’ll need internet connection for loading
libraries and data. Ensure that R is installed and functional on your
system. Familiarize yourself with basic statistical concepts such as
hypothesis testing, and p-values as these will provide a foundation for
understanding multiple testing procedures.
Recommended prior learning: