The scientific approach structures every research project, from university theses to industrial R&D programs. Its principles are based on a logical sequence: observe, question, test, conclude. Since the revision of the European Code of Conduct for Research Integrity by ALLEA in 2023, four requirements permeate all stages – reliability, honesty, respect, responsibility. Understanding each link in this chain equips one to produce solid and reproducible results.
1. Observation of the phenomenon: the starting point of any research
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Every scientific approach begins with a careful observation of reality. It is not a passive glance: the researcher identifies a regularity, an anomaly, or a discrepancy between what is expected and what occurs. This phase engages both the senses and measuring instruments.
Observation requires distinguishing the raw fact from its interpretation. A thermometer displaying an unexpected value is a fact. The explanation for this discrepancy belongs to the next stage. Confusing the two leads to what cognitive bias specialists call confirmation bias, where one only sees what supports a pre-existing idea.
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To deepen this logical progression, detailing the stages of the scientific approach on Job ‘n Roll helps situate each phase within an operational framework.
2. Formulation of the problem: transforming observation into a precise question
An observation only becomes usable once it is converted into a testable question. The problem delineates the scope of the research and guides the choice of methods. A question that is too broad (“why is the climate changing?”) prevents any rigorous experimentation.
A good problem targets a precise link between two variables. For example, instead of “what is the effect of light on plants?”, a biology researcher would formulate: “does the duration of light exposure influence the germination speed of this species?”. This level of precision conditions the quality of everything that follows.
3. Literature review: mapping the state of knowledge
Before constructing a hypothesis, one must know what other researchers have already established. The literature review prevents the replication of experiments already conducted and allows for the identification of remaining areas of uncertainty.
This step has become more demanding since the inclusion of scientific integrity in the French Research Code (article L.211-2), stemming from the 2021-2030 Research Programming Law. Institutions must now appoint a scientific integrity referent and adopt a formal policy for handling breaches. The literature review is directly integrated into this framework: correctly citing sources and not omitting contradictory results are obligations, not options.
The rise of open science, integrated into the ALLEA revision of 2023, facilitates access to data. However, this abundance necessitates a methodical sorting to distinguish peer-reviewed publications from unverified preprints.
4. Construction of the hypothesis: proposing a testable explanation
The hypothesis is a provisional answer to the problem. It must meet two conditions: it must be falsifiable (one can imagine a result that would contradict it) and operational (one can design an experiment to test it).
A non-falsifiable hypothesis does not belong to the scientific domain. It is the criterion of falsifiability, inherited from the work of Karl Popper, that separates the scientific approach from other forms of reasoning. Formulating “this molecule reduces the proliferation of cells of this lineage under these conditions” is testable. Formulating “this molecule has a positive effect on health” is not, as it is too vague to be disproven.
5. Experimental protocol: designing a reproducible experiment
The protocol details the exact conditions of the experiment: measured variables, control group, sample size, tools used, duration. Its primary objective is reproducibility: another researcher, in another laboratory, must be able to repeat the same experiment and obtain comparable results.
A rigorous protocol anticipates biases. Among the most common:
- The selection bias, which occurs when the sample does not represent the studied population
- The placebo effect, neutralized by double-blind procedures in biomedical sciences
- The measurement bias, related to a poorly calibrated instrument or an inconsistent data collection procedure
The decree of December 3, 2021, strengthens these requirements by imposing a data management policy on institutions from the design of the protocol.
6. Analysis of results: interpreting without extrapolating
Raw data do not speak for themselves. Statistical analysis helps determine whether the observed results are significant or if they are due to chance. This step requires choosing the appropriate statistical methods based on the type of data and sample size.
A negative result holds as much scientific value as a positive result. Disproving a hypothesis advances knowledge by eliminating one avenue. Field feedback diverges on this point: in practice, negative results remain under-published, creating a publication bias detrimental to the scientific community.
The analysis must also distinguish correlation from causation. Two phenomena that vary together are not necessarily linked by a cause-and-effect relationship.
7. Communication and peer review: submitting work for critique
The final step closes the loop: results are published, submitted to peer review, and then discussed by the scientific community. This critical review process allows for the detection of methodological errors, abusive interpretations, or incomplete data.
The ALLEA revision of 2023 emphasizes transparency at this stage. The four principles of the European Code, reliability, honesty, respect, and responsibility, apply equally to the author and the reviewer. A reviewer must disclose any conflicts of interest, and an author must make their data accessible in a spirit of open science.
- Deposit datasets in an open repository to allow verification
- Describe the limitations of the study in the discussion section of the article
- Respond to post-publication comments transparently
The scientific approach is not a fixed linear path. Each step can refer back to a previous one: an unexpected result reignites observation, a fragile analysis requires revisiting the protocol. It is this capacity for self-correction, recently framed by legal obligations in France and revised European standards, that distinguishes scientific production from mere accumulation of information.