LMS Introduction to statistics & hypothesis testing

Jesús Urtasun Elizari - LMS bioinformatics

Find the content of the course in GitHub:

https://github.com/LMSBioinformatics/statistics_hypothesis_testing

This course provides an introduction to the field of probability, statistical theory of sampling, parameter estimation and hypothesis testing. The course is organized in six chapters, covering the following topics.

Roadmap of the course

Chapter 1. Introduction to random variables & probability distributions.

• Random variables and probability theory.
• Discrete probability distributions.
• Continuous probability distributions.

Chapter 2. The Central Limit Theorem.

• The Central Limit Theorem.
• Confidence intervals.
• The z distribution and student's t distribution.

Chapter 3. Introduction to hypothesis testing.

• Null and alternative hypothesis.
• Significance and p-values.
• Comparing two means. Two sample t-test.

Setup

We will be working with jupyter notebooks. The easiest way to access jupyter is via the Anaconda platform. Please install Anaconda from https://www.anaconda.com in advance of the workshop. NB no knowledge of programming is required for this workshop.

Getting Started

1. Download this repository to your computer as a ZIP file and unpack it.

2. Open Rstudio and navigate to the different chapters.

3. Alternatively, you can run the notebooks online using Binder: