Introductory description

The aim of this module is to give students a basic understanding of the statistical methods appropriate to data analysis in analytical science, and to provide guidance on some statistical tools for more advanced study. Topics include: basic probability; error analysis and calibration; summarising data and testing simple hypotheses; statistical computing (software and practice, including simple graphics); experimental design and analysis of variance; sampling methods and quality control; simple analysis of multivariate data. Each session will combine lecture and data analysis workshop. At the end of the course the student should be able to appreciate the added value that statistical analysis can bring to research to perform basic statistical analyses of simple data sets using statistical software to design simple experiments.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

The role of statistics within science and the nature of measurement
Summarising data
Probability and distributions
Testing hypotheses
ANOVA and linear models
Statistical computing
Designing experiments

A selection of advanced material will be presented to the students from the list below, the aim of which is to give students an entry level understanding of the advanced statistical topics that they might meet during their research careers.

Advanced material:
The analysis of designed experiments
Statistical modelling and model choice
Analyses for non-Normal data
Markov chains and stochastic processes
Multivariate methods and clustering
Bootstrap methodology
Introduction to Bayesian statistics and modern computational statistics.

Learning outcomes

By the end of the module, students should be able to:

• (a) Subject knowledge and understandingBe familiar with key probability distributions.Be familiar with commonly used statistical methods.
• (b) Key SkillsUse statistical methods to determine data quality and extract key quantities of interest.Perform parameter estimation. Able to formulate and implement an appropriate hypothesis test to determine statistical significance.Use appropriate statistical software.
• (c) Cognitive SkillsUnderstand how probability can be used to describe variation in experimental data.Understand how hypothesis testing can be used to quantify the amount of evidence in favour of a scientific hypothesis.
• (d) Subject-Specific/Professional SkillsKnow how to analyse data from a range of techniques.Summarise data, implement parameter estimation and appropriate statistical tests.Follow discussion of more advanced statistical techniques in the scientific literature.

Subject specific skills

(a) Subject knowledge and understanding
Be familiar with key probability distributions.
Be familiar with commonly used statistical methods.

(b) Key Skills
Use statistical methods to determine data quality and extract key quantities of interest.
Perform parameter estimation.
Able to formulate and implement an appropriate hypothesis test to determine statistical significance.
Use appropriate statistical software.

(c) Cognitive Skills
Understand how probability can be used to describe variation in experimental data.
Understand how hypothesis testing can be used to quantify the amount of evidence in favour of a scientific hypothesis.

(d) Subject-Specific/Professional Skills
Know how to analyse data from a range of techniques.
Summarise data, implement parameter estimation and appropriate statistical tests.
Follow discussion of more advanced statistical techniques in the scientific literature.

Transferable skills

1 Critical thinking

• Recognise patterns, themes and key messages from sometimes confused and incomplete data.
• Make informed decisions on the value of a range of sources allowing an evidence based conclusion based on this analysis.
  2 Problem solving
• Use rational and logical reasoning to deduce appropriate and well-reasoned conclusions.
• Retain an open mind, optimistic of finding solutions, thinking laterally and creatively to look beyond the obvious.
• Knows how to learn from failure.
  4 Communication
• Communicate orally in a clear and sensitive manner which is appropriately varied according to different audiences.
• Written: Present arguments, knowledge and ideas, in a range of formats. Active listening: questioning, reflecting, summarising.
  7 Digital literacy
• Has the capabilities that enable living, learning and working in a digital society.
• Comfortable with using digital media to communicate, solve problems, manage information, collaborate, create and share content.
  11 Professionalism
• Prepared to operate autonomously.
• Aware of how to be efficient and resilient.
• Manages priorities and time.
• Self-motivated, setting and achieving goals, prioritising tasks.