- Course overview
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- Environmental DNA
- DNA metabarcoding and its applications
- Workflow for eDNA metabarcoding
- DADA2 for analysing metabarcoding data
- Taxonomic classification to assess biodiversity
- Advances in biodiversity exploration
- Open data resources for eDNA
- Bringing data to life: Data management and sharing
- Further learning
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- References
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Statistics principles in data analysis
Biostatistics is a vital part of data-driven life science because it provides the foundational tools and methods for designing experiments, analysing data, and interpreting results with accuracy and precision. It ensures that the research is scientifically valid by managing variability and uncertainty inherent in biological data. By applying statistical methodologies, researchers can draw reliable conclusions, enhance the reproducibility of their studies, and communicate findings effectively.
In the following video, recorded on 23 May 2023, Sarah Kaspar explains on what you should pay attention to when you interpret results from a biostatistical analysis (Kasper S, 2023). You can watch this video to learn about the general concepts behind hypothesis testing workflow, including summary statistics, null and alternative hypotheses, and p-values.
To explore further the common principles of statistical methods and to gain practical experience using user-friendly tools for data visualisation and testing please go through the learning resource (Kasper S, 2022) provided below.
After completing the practical introduction to biostatistics, proceed to the next page to watch a webinar recording, which explains statistical concepts applied to microbiome data analysis.