Choosing the topic
Topic selection is one of the most important parts of an IA. A suitable topic makes both the experiment and the analysis easier. Students sometimes believe that they must invent an experiment that has never existed before, but an IA does not require doctoral-level originality.
One student investigated how acids and bases affected the strength of slime. It was a creative idea, but finding published data for comparison was difficult. When direct reference data are unavailable, related research can still help explain the chemistry and support the analysis.
A strong topic can come from a familiar textbook experiment that is adapted to a real-life question. For example, a magnesium–hydrochloric acid kinetics experiment could be extended using temperature and the Arrhenius equation. In Biology, a familiar digestion experiment could be adapted to compare digestive enzymes with enzymes found in a plant such as papaya.
Three useful rules are: do not make the topic unnecessarily grand; review several previous IAs before deciding; and look for ways to connect a syllabus experiment to a practical context. Clastify and InThinking were mentioned in the original article as places to review examples, but students should also follow their school's current guidance.
Review IA examples on Clastify ↗Visit InThinking ↗
Planning the experiment
Once you have several possible topics, search for related papers and established experimental methods. A method does not need to be completely original. Using a well-supported procedure—and adapting it carefully to the research question—is often safer than starting with an experiment for which no comparable work can be found.
Before collecting data, define the independent and dependent variables, identify the controlled variables, and plan how uncertainty and error will be treated. Think about whether the experiment will produce values that can be analysed clearly. Repeatedly transferring or measuring a quantity with several pieces of equipment may increase random uncertainty, so the procedure should be designed with this in mind.
Record the real procedure and measurements carefully. Filming the experiment can provide a useful record of what was actually done. If results look unusual, investigate possible reasons instead of changing or inventing values. Honest anomalies can become valuable material for the evaluation.
Writing the investigation
The opening should explain briefly and specifically why the investigation matters. Personal engagement should not become a full autobiography. A short explanation of what led to the question, supported by a relevant paper or textbook idea, is usually more effective.
Background information should include only the scientific ideas needed to understand the investigation. Cite reliable sources, explain important equations or molecular structures, and make the section clear enough that the reader does not need to reconstruct the logic. Methodology and safety precautions should be specific to the investigation rather than a list of generic laboratory rules.
Tables can make variables and data easier to read, but formatting must remain clean. A focused investigation often works best with one independent variable, one dependent variable, a sensible range of values, repeated trials, and an average. Use units and significant figures consistently from the beginning.
Analysis, uncertainty, and evaluation
The analysis should bring the calculations together and explain what the data mean. Use an appropriate best-fit line where relevant, report the coefficient of determination only when it is meaningful, and discuss reliability and anomalies in the context of the experiment.
Random uncertainty can be discussed through measurement uncertainty and propagated percentage uncertainty. Systematic error may be evaluated by comparing an experimental result with a reliable literature value and calculating percentage error when such a comparison is scientifically valid. Distinguish clearly between these two kinds of error.
After calculating percentage uncertainty, convert it to an absolute uncertainty where appropriate and present the final result with sensible precision. The conclusion should answer the research question using the evidence, while the evaluation should connect specific limitations to realistic improvements.
Final review
The last stage is the part many students least enjoy: checking the English, scientific accuracy, citations, calculations, layout, and current IB requirements as one complete piece of work. A strong IA feels easy to read because every section has a purpose and the reasoning moves forward without unnecessary material.
Students should remain responsible for their own research question, experimental work, data, analysis, and final writing. Feedback is most useful when it helps the student identify weaknesses and revise the work independently rather than replacing the student's authorship.
Learn the required format before writing, plan the analysis before running the experiment, and leave enough time for a careful final review. That preparation makes the IA much more manageable.