Analysing your own data: a workshop on statistics and experimental design (IN-PERSON)

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This week-long course is aimed at people with little or no experience using statistical analyses in research. It introduces participants to core concepts in statistics and experimental design, aimed at ensuring that the resulting data is able to address the research question using appropriate statistical methods.

The interactive course gives participants a hands-on, applied foundation in statistical data analysis and experimental design. Group exercises and discussions are combined with short lectures that introduce key theoretical concepts. Computational methods are used throughout the course, using the R programming language. Formative assessment exercises allow participants to test their understanding throughout the course and encourage questions and critical thinking.

By the end of the course participants will be able to critically evaluate and design effective research questions, linking experimental design concepts to subsequent statistical analyses. It will allow participants to make informed decisions on which statistical tests are most appropriate to their research questions. The course will provide a solid grounding for further development of applied statistical competencies.

If you do not have a University of Cambridge Raven account please book or register your interest here.

• The course is aimed at people at postgraduate level who are involved in research.
• Applicants are expected to have a working knowledge of R.
• The course is open to Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
• Further details regarding eligibility criteria are available here
• Guidance on visiting Cambridge and finding accommodation is available here.

Please note that all participants attending this course will be charged a registration fee. The fees are as follows:

• Industry participants (non-academic) pay £600
• Members of the University of Cambridge and participants from other academic institutions pay £300

⚠ Bookings are only approved and confirmed once you have paid the fees in full.

• Working knowledge of R and the tidyverse package.
• This course is not suitable for people who have completed either the Core Statistics or Experimental Design for Statistical Analysis courses, since significant portions of the course borrow from these stand-alone courses.

Number of sessions: 5

Statistics, Experimental Design, R

During this course you will learn about:

• One and two sample hypothesis tests
• ANOVA
• Simple linear Regression
• ANCOVA
• Linear Models
• Model selection techniques
• Power Analyses
• Practices in experimental design that lead to high quality research
• What to do with more advanced analysis techniques for experiments with unusual or complex designs
• How to take power analysis into consideration in your experimental design
• How to implement piloting in your experiments

After this course you should be able to:

• Analyse datasets using standard statistical techniques
• Know when each test is and is not appropriate
• Link experimental design to your statistical analysis strategy
• Formulate good research questions
• Identify common design pitfalls, and how to avoid or mitigate them
• Operationalise variables effectively
• Identify and deal with confounding variables and pseudoreplication

Presentations, demonstrations, discussions and practicals

All days will run from 09:30 - 17:00.

There will be regular breaks, with coffee/tea and biscuits provided throughout the day.

A lunch break is scheduled around 12:30 - 13:30.

Further information on catering will be provided closer to the start of the course.

5

• Applied Statistics
• Bioinformatics