IB Chemistry HL Internal Assessment Example

The research question is clearly framed around a specific biological system, time range, and intervention set. By naming the banana species, the 0–4 day window, and the three storage treatments, the student gives the investigation a focused scope that is appropriate for a chemistry-based study.

The student justifies the chosen observation window by explaining that the design captures early ripening and the transition where treatments are likely to diverge. That rationale strengthens the methodological logic because it shows the data collection period was selected to match the biological process being studied.

The controlled-variables table is a real strength because it does more than list variables: it links each control to a reason for protecting validity. This shows the student understands why temperature, sample mass, and timing matter rather than treating them as checklist items.

The controlled-variables table would be stronger if it included the actual recorded temperature and humidity values rather than only the intention to control them. Without logged values, the reader cannot judge how stable the environment really was or how much it may have influenced ripening.

The equipment list still leaves some uncertainty about the intervention materials, especially because the write-up later switches between foil types. Since gas permeability affects ripening, the student should specify exactly which material was used for stem wrapping and whether it was standardized across all samples.

What would happen to the validity of the comparison if the bananas assigned to one treatment started at noticeably different ripeness levels from the others?

The sample-preparation steps are detailed enough to make the investigation broadly reproducible, especially the fixed 50.00 g mass and the repeated blending before measurement. That consistency matters because it reduces variation introduced by sample handling.

The instruction to place bananas in “tightly closed” bags is not standardized enough for full reproducibility. The student should state how closure tightness, bag size, and airflow were controlled, because small differences in ventilation could change ripening rate.

The method still does not justify the use of only one banana per treatment per day. This is a major design limitation because it means one fruit can determine the result for an entire group, so the student should explain replication strategy or acknowledge that biological power is limited.

Consider adding a brief note on randomization, such as assigning bananas to treatments randomly from each bunch. That would reduce selection bias and make the treatment comparison more convincing.

The treatment setup is clearly described for the three groups, so the reader can see how the external factors were applied. This makes the design easier to follow and helps distinguish the independent variable from the measurement procedure.

The evaluation identifies weaknesses, but it does not rank them by impact. The most serious issue is the lack of biological replicates and randomization, because that affects the entire comparison, so the student should explain which limitation matters most and why.

The weakness table is constructive because it names concrete limitations such as prism cleanliness and air bubbles rather than using generic statements. That specificity makes the evaluation more scientifically useful because each weakness can be linked to a particular source of error.

The strengths table names valid positives, but it does not explain how each strength affected the final reliability of the investigation. The student should move from listing strengths to showing their actual significance for the quality of the data.

The improvement column is helpful, but some suggestions remain too general to show full evaluation. For example, the student should explain how a given change would reduce systematic error, random error, or bias, not just state that it would be better.

The explanation of bubble interference is well linked to the measurement mechanism, which makes the improvement meaningful rather than decorative. This is a good example of connecting a limitation to how the instrument actually works.

The evaluation goes beyond simple listing by identifying that bananas from the same bunch may still differ in glucose content. This shows awareness that biological variability can affect reliability even when the method is otherwise controlled.

Which limitation most threatens the overall conclusion: instrument error, biological variation between bananas, or the use of a single fruit per treatment? Why?

Consider restructuring the evaluation so each weakness is followed immediately by its likely effect on validity or reliability and then by the improvement. That format would make the relative importance of each issue much clearer.

The improvement discussion would be stronger if it connected each change to a specific weakness already identified in the table. At the moment, the advice is relevant, but the chain from problem to fix to expected improvement is only partly explicit.