The scientific approach structures the production of knowledge in all experimental disciplines. Whether in physics, biology, or social sciences, the method relies on a logical sequence in four stages: formulating a question, proposing a hypothesis, testing through experimentation, and then analyzing the results. This framework does not vary according to the level of study or the field of research, but the rigor with which each step is conducted determines the reliability of the conclusions.
1. The question or problem statement: the starting point of any research
Every scientific approach begins with an observation that raises a problem. An unexpected phenomenon, a contradiction between two known data points, or an unexplained fact: the research question arises from this gap between what is observed and what is understood.
The quality of the question conditions the entirety of the approach. A problem statement that is too vague (“why is the climate changing?”) does not allow for the design of a protocol. An operational question targets a specific phenomenon within a defined context. For example: “Does the temperature of the water influence the rate of salt dissolution?” This formulation directly leads to a feasible experiment.
A common pitfall at this stage is to pose a question that already contains its answer. If the problem presupposes a result, it biases the entire process. To avoid this, the question must remain open and verifiable. An article detailing the steps of the scientific approach on Career Trotter reminds us that the problem statement must be testable by concrete means; otherwise, it falls under opinion rather than science.
In recent school programs, expectations are organized by year rather than by cycle, which allows for a clearer progression in formulating scientific problems according to the students’ level.
2. The hypothesis: a provisional answer to be tested
The hypothesis is an explanatory proposition formulated before any experiment. It answers the initial question in the form of a verifiable statement: “I think that the high temperature of the water accelerates the dissolution of salt.” It is neither a certainty nor a vague intuition, but a prediction that engages a specific mechanism.
Formulating a good hypothesis requires two qualities:
- It must be falsifiable. If no experiment can contradict it, it has no scientific value. Karl Popper formalized this criterion of falsifiability, which has become a methodological benchmark in most disciplines.
- It must be consistent with existing knowledge. A hypothesis that completely breaks with established knowledge is not prohibited, but it requires a particularly robust protocol to be taken seriously.
- It must be formulated in a way that clearly guides the choice of experimental protocol. A vague hypothesis produces a vague experiment.
The recent trend towards pre-registering hypotheses reinforces this step. In the context of Registered Reports, researchers document their hypotheses, methods, and planned analyses before data collection. The decision to publish then rests on the question and the method, not on the positivity of the results. This system helps distinguish planned tests from exploratory analyses conducted afterward.
3. Experimentation: putting the hypothesis to the test of facts
Experimentation is the moment when the hypothesis leaves the realm of reflection to confront reality. It involves designing a protocol, gathering materials, performing manipulations, and collecting measurable data.
A rigorous protocol isolates only one variable at a time. To test the effect of temperature on salt dissolution, the amount of water, the mass of salt, and the method of agitation must remain identical from one trial to another. Only the temperature changes. Without this control, the results do not allow for conclusions.
The experimental protocol is written like a reproducible recipe:
- Each step begins with an action verb in the infinitive (“pour,” “measure,” “heat”).
- The list of materials is exhaustive, accompanied if necessary by diagrams.
- The measurement conditions (instruments used, units, number of repetitions) are specified before the manipulations begin.
Reproducibility does not depend solely on the protocol itself. The FAIR principles (Findable, Accessible, Interoperable, Reusable) recommend documenting and preserving data so that other researchers can verify or reanalyze the results. Publishing results without making data verifiable weakens the significance of any conclusion.
In a school context, experimentation can also take the form of systematic observation, simulation, or documentary research, depending on available resources. The diversity of approaches (manipulation, modeling, documentation) simultaneously develops rigor and critical thinking.
4. Analysis and conclusion: interpreting the results without forcing them
Analysis involves comparing the results obtained with the prediction formulated in the hypothesis. The data are organized, often in the form of tables or graphs, and then interpreted. The hypothesis is then confirmed, nuanced, or refuted by the observed facts.
A result that contradicts the initial hypothesis is not a failure. In science, refuting a proposition is as valuable as confirming one. The problem arises when the results are ambiguous: imprecise data, a sample that is too small, or an uncontrolled bias can make interpretation uncertain. In this case, an honest conclusion involves acknowledging the limitations and proposing improvements to the protocol.
Analysis leads to the construction of a model or theory when the results are solid and reproducible. This model remains provisional: it will be revised if new data contradict it. The scientific approach is cyclical, not linear. A conclusion often opens up a new question, restarting the process from the beginning.
Communicating results is an integral part of this final step. Open access to publications is progressing (the National Cancer Institute indicates that a large majority of its funded publications are now open access), but making an article available does not guarantee reproducibility. Without raw data and detailed protocol, peer verification remains incomplete.
These four steps form a demanding framework, applicable from middle school to research laboratories. Their strength lies in their internal logic: each step follows from the previous one and prepares for the next. Altering the order or skipping a phase weakens the entire approach, regardless of the subject studied.
