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Doctoral and Postdoctoral researchers from all disciplines working with data. No previous knowledge of statistics or Python are required
This course aims at understanding fundamental concepts in probability and statistical inference, as well as its application using Python.
The course uses a combination of lectures and participatory activities to understand the principles of statistical inference. It will answer questions such as: “Why do I need statistics?”, “What’s in a p-value?”, “How many replicates do I need”, and hopefully help avoid questions such as “Why are my 5 years of data useless?”
The course will cover all steps of statistical inference: design of experiments, data acquisition, basic statistical analyses, and basic graphs. A large portion of the course will be dedicated to practical exercises using the language Python. The course will also include instruction on the use of Python along every step of the process. At the end of the course, participants will have the opportunity to do a project with their own data (or freely available data).
Understand probability and probability distributions
Perform and interpret descriptive statistics
Use and interpret basic statistical tests
Use and interpret (Ordinary Least Squares) linear regression
Experimental design (with relevant to most people in hydrology/hydrogeology)
> More information, Module 2017-35