Experimental Design — A strong experimental design reduces ambiguity. It defines what changes, what remains controlled, what will be measured, which comparisons are necessary and how results will be interpreted before data collection begins.
Objective, hypothesis and variables
Sandra helps convert a broad idea into a testable objective. Independent, dependent and confounding variables can be identified together with reference conditions and controls. This prevents the final analysis from trying to answer questions the experiment was never designed to resolve.
Controls, replication and comparisons
Reproducibility depends on practical choices. Depending on the project, these may include replication, randomization, run order, positive or negative controls and exclusion criteria. There is no universal template; decisions should follow the question, resources and expected evidence.
Measurement and analysis planning before the experiment
Defining what will be measured and how outcomes will be summarized reduces opportunistic interpretation. Sandra can help structure capture tables, units, codes, quality criteria and a preliminary analysis plan, while documenting known limitations.
Clear protocols and traceability
A good protocol lets another person understand the work sequence and critical decisions. Documentation may include materials, preparation, conditions, timing, control points, deviations and result structure. This supports traceability in both academic and applied work.
How the work can be organized
The process can begin with a diagnostic consultation and continue in stages: objective review, assessment of available documents or data, scope definition, technical work, result review and delivery. Support is remote and can cover one stage or a broader project.
Related support is also available through laboratory research, research methodology and research for companies.
Frequently asked questions
Can an experiment be reviewed before it is run?
Yes. Reviewing objectives, variables and measurements before generating data often prevents expensive changes later.
Does experimental design always require advanced statistics?
No. Complexity depends on the question. A logical and feasible design comes first, followed by an appropriate analysis plan.
Can this be used for laboratory experiments?
Yes. The approach can be adapted to laboratory, food, agro-industrial and other scientific projects.