About the course | Intended audience | Prerequisites | Content details
About the course
A practical introduction to experimental design for researchers working in the life sciences and related disciplines.
This course combines key theoretical concepts with practical application to help researchers design effective, statistically robust experiments. The emphasis throughout is on linking experimental design decisions to a clear and appropriate analysis strategy.
Topics include formulating research questions, operationalising variables, identifying confounding variables, avoiding pseudoreplication, power analysis, piloting, and approaches for handling more complex or unusual experimental designs.
By the end of the course, participants should be able to:
- link experimental design decisions to an appropriate statistical analysis strategy
- formulate clear and effective research questions
- identify common experimental design pitfalls and avoid or mitigate them
- operationalise variables appropriately for data collection and analysis
- identify confounding variables and pseudoreplication in experimental studies
- understand how power analysis and piloting contribute to robust experimental design
The course is delivered through a mixture of lectures, group discussion, and worked examples using realistic research scenarios.
Back to top Intended audience
This course is suitable for:
- researchers and postgraduate students designing biological or experimental studies
- participants planning laboratory, field, or observational experiments
- researchers who want to improve the statistical quality and reproducibility of their work
- participants seeking a stronger understanding of how experimental design affects downstream data analysis
Although examples are primarily drawn from the life sciences, many of the concepts covered are applicable across a wide range of research disciplines.
Prerequisites
No advanced statistical training is required, although some familiarity with basic statistical concepts will be helpful.
This course is aimed at non-specialists and focuses on practical understanding rather than mathematical detail.
Content details
The course covers the following topics:
- Formulating research questions
Introduces the foundations of good experimental design by focusing on how to develop clear, answerable, and testable research questions. Participants learn how research aims influence experimental structure, data collection, and analysis strategies.
- Operationalising variables
Explores how abstract biological or experimental concepts can be translated into measurable variables. The session focuses on selecting appropriate response and explanatory variables and ensuring that measurements are suitable for later statistical analysis.
- Experimental units and replication
Covers the importance of identifying the correct experimental unit and implementing appropriate replication. Participants learn how replication improves reliability and how incorrect replication can undermine statistical inference.
- Confounding variables
Introduces confounding variables and their impact on experimental interpretation. Participants learn practical strategies for identifying, controlling, randomising, or accounting for confounders during the design stage.
- Pseudoreplication
Explains pseudoreplication and why non-independent observations can produce misleading results. The session uses applied examples to demonstrate how pseudoreplication arises and how it can be avoided in practice.
- Linking design to analysis
Focuses on the relationship between experimental design and statistical analysis. Participants learn how design choices determine which analyses are appropriate and why planning the analysis strategy in advance is essential for robust research.
- Power analysis and sample size
Introduces the principles of statistical power and sample size planning. Participants learn how effect size, variability, and replication influence the ability to detect meaningful effects and how power considerations can guide practical experimental decisions.
- Piloting experiments
Covers the role of pilot studies in refining experimental protocols, estimating variability, and identifying practical issues before larger-scale data collection begins.
- Complex and unusual experimental designs
Provides a brief introduction to more advanced analysis approaches used in experiments with non-standard or complex designs. The emphasis is on recognising when standard methods may not be appropriate and understanding the implications for data analysis and interpretation.
- Good practice in experimental design
Brings together the principles covered throughout the course to highlight practical approaches that improve research quality, reproducibility, and interpretability.