Students will be introduced to principles of research design and quantitative data analysis. The course will start with data wrangling skills; data visualisation and descriptive statistics and then introduce hypothesis tests. Basic inferential statistical techniques will be covered including t-test, correlation, regression, chi-squared tests and one way ANOVAs. Students will learn to organise, visualise and summarise data and conduct inferential tests in R and produce reproducible reports using Rmarkdown.

Fourteen one-hour weekly lectures followed by practicals using R studio. The lectures will introduce different analysis methods, focusing on understanding how each is applied to answer different questions and fostering understand of the conclusions that could be drawn from results. The practical classes will give students experience of running each analysis using real datasets and writing scripts to produce reproducible analysis pipelines that include data wrangling, data visualization, descriptive statistics, inferential statistics and communication of results. Students will complete weekly between each class to consolidate their learning.

Aims

To teach the principles of research across a range of methodologies, so that students can understand the literature related to their other courses.
	To introduce students to commonly used inferential statistical tests and to the packages/functions in R to perform these tests. Special attention will be paid to different effect size measures and their interpretation.
	To enable students to understand and evaluate the research methodology and statistical procedures used by researchers when reading research papers in their studies.
	To provide students with the necessary skills needed to design, execute, analyse and communicate the results of their research projects in the third year.
	To encourage students to apply the statistical knowledge and expertise learned in this course to data generated in other units.
	To teach students fundamental research methodology and coding skills so that they can consider undertaking relevant research in their future careers.
Outcomes

General: 

The skills learned in this module are relevant to all the other 1st, 2nd and 3rd year modules as they will enable students to assess research designs in assigned reading and to evaluate the statistics and conclusions of the experimenters. In addition, this course provides fundamental research skills that will enable students to design a study and conduct analyses for their 3rd year research project and to interpret the relevant literature.

Specific Skills: 

Evaluate research reported in research papers.
	Interpret tables and graphs and select the appropriate graphs for displaying different types of data.
	Select the most suitable statistical test taking into account the experimental design, the type of data and the research question(s).
	Report the outcome of statistical tests accurately and clearly.
	Understand the limitations of statistical tests (e.g. statistical power).
	Write reproducible scripts in R for data wrangling, visualization, descriptive statistics and inferential tests.
These outcomes will be achieved through:

Teaching of the syllabus outlined below,
	Guidance and formative feedback from tutors on non-assessed exercises in practical computing workshops,
	Computer assisted learning exercises set for study time