Introduction to statistics

Trainer: Sarah Kaspar

Overview: In this session, you’ll begin by learning what statistical inference is, and explore some illustrative examples. You’ll then delve into key concepts such as statistical distributions, sampling, and the principles of hypothesis testing, supported by worked-through examples. Finally, in a practical session, we’ll apply what we’ve learned by analyzing a real dataset using Python.

By the end of this session, you will be able to:

  • Discuss key concepts in statistical inference, including distributions, sampling, and hypothesis testing, using examples from biological data.
  • Apply the statistical concepts covered in the session to real datasets using Python.

Materials:

The ‘Statistics with Python’ link will redirect you to a Google Colab file.

Never used Google Colab before?

Google Colab is a free cloud-based service that allows you to write and execute Python code in your web browser, similar to what Google Docs allows for writing. To run and modify the code provided here, you can:

  • Download the Colab file as a .ipynb file, and open it with Jupyter Notebook. You can then work on the code on your own machine. Please note that by doing so you may have to manage independently the code dependencies.
  • Create your own private copy of the Colab in your Drive

For more info and guidelines on how to install Jupyter Notebook, you can use visit the  the “Installing Jupyter” page of their website.