Experimental design for statistical analysis (IN-PERSON)

IntermediateNew

This one-day course is primarily aimed at life science researchers, but covers many topics that are applicable to other fields. It combines key theoretical knowledge with practical application, which will aid researchers in designing effective experiments. The focus throughout the course is to link experimental design to a clear analysis strategy. This ensures that the collected data will be suitable for statistical analysis. During this course, we cover:

• Practices in experimental design that lead to high quality research
• Common design pitfalls, and how to avoid or mitigate them
• A brief introduction to more advanced analysis techniques for experiments with unusual or complex designs

Topics included in the course include: crafting a good research question, operationalising variables effectively, identifying and dealing with confounding variables and pseudoreplication, and practical tips for power analysis and piloting.

The course is delivered via a mix of lectures, group discussion and worked examples.

The training room is located on the first floor and there is currently no wheelchair or level access available to this level.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

• Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
• Please be aware that these courses are only free for University of Cambridge students. All other participants will be charged a registration fee in some form. Registration fees and further details regarding the charging policy are available here.
• Further details regarding eligibility criteria are available here

Basic statistical literacy is required, such as obtained through attendance of the Core statistics course, or equivalent. No prior programming knowledge is required, although a working knowledge of R or Python is useful, as the provided worked examples are based on these languages.

Number of sessions: 1

After this course you should be able to:

• 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

During this course you will learn about:

• 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
• How to implement piloting in your experiments

The course is delivered via a mix of lectures, group discussion and worked examples.

• Free for registered University of Cambridge students
• £ 50/day for all University of Cambridge staff, including postdocs, temporary visitors (students and researchers) and participants from Affiliated Institutions. Please note that these charges are recovered by us at the Institutional level
• It remains the participant's responsibility to acquire prior approval from the relevant group leader, line manager or budget holder to attend the course. It is requested that people booking only do so with the agreement of the relevant party as costs will be charged back to your Lab Head or Group Supervisor.
• £ 50/day for all other academic participants from external Institutions and charitable organizations. These charges must be paid at registration
• £ 100/day for all Industry participants. These charges must be paid at registration
• Further details regarding the charging policy are available here

1

Several times a year

• Core Statistics using R (IN-PERSON)