### Outline

This three day course is intended to open doors to applying statistics - whether directly increasing skills and personally undertaking analyses, or by expanding knowledge towards identifying collaborators. The end goal is to drive confident engagement with data analysis and further training - increasing the quality and reliability of interpretation, and putting that interpretation and subsequent presentation into the hands of the researcher. Each day of the course will deliver a mixture of lecture, workshop and hands-on practice.

### Instructors

### Keynote lectures

### Aims

During this course you will learn about:

- The importance of Reproducible Research and how tools such as R can help
- Planning your experiment and why good experimental design is critical
- How to use spreadsheet programs (such as Excel) more effectively, and the limitations of such programs
- Writing and executing basic data analysis workflows in R
- Formulating and interpreting the result of a statistical test
- Choosing the appropriate graphics to understand and present your data

### Objectives

After this course you should be able to:

- Identify sources of variation and confounding in your experimental design and prepare for a consultation with a statistician
- Assess the distribution of your data and choose the appropriate statistical test; recognising any limitations that may exist
- Create a reproducible piece of R code to import, visualise and perform a statistical test on biological data
- Know how to develop your data analysis skills after the course

### Materials

## Day One

## Day Two

- Introduction to Statistical Thinking (lecture)
- Introduction to Rmarkdown and Statistics in R (practical)
- Introduction to Rmarkdown and Statistics in R (answers)
- Hypothesis Testing (lecture)
- Simple hypothesis testing in R (practical)
- Simple hypothesis testing in R (answers)

## Day Three

- Regression analysis in R (lecture)
- Overview of linear modelling (practical)
- Overview of linear modelling (answers)
- Introduction to Scientific Figure Design (lecture)
- Introduction to Scientific Figure Design (practical)

### Shiny apps

To demonstrate some of the statistical concepts in the course, we have developed some Shiny apps.- The Central Limit Theorem
- Descriptive Statistics
- Standard Error versus Standard Deviation
- Data Exploration
- Linear Regression
- Simulating the p-value