IB Physics HL Internal Assessment Example

The introduction places the question in a relevant real-world and scientific context by linking it to surface tension, paint lids, and water striders. That context helps justify why the investigation matters beyond the classroom.

The research question is sharply focused on two measurable variables: disk area and maximum detachment force. This makes the investigation easy to follow and gives the later data processing a clear target, which is a strong foundation for the design.

The student clearly states the experimental focus before introducing prior research. This signposting helps the reader see how the theory section connects to the investigation rather than reading theory in isolation.

This is an important theoretical claim, but it is not explained clearly enough here. The student should clarify how a perimeter-based relationship can support an area-based investigation, because that distinction becomes central to the validity of the research design.

The method identifies how the independent variable changes, but it is still too vague for exact replication. The student should specify the plastic film type, thickness, and how the disks were cut and standardized, because those properties could affect stiffness and detachment force.

This control is only loosely described. The student should state how temperature was measured and whether it was monitored across all trials, since surface tension is temperature-sensitive and small changes could shift the results.

Consider adding a defined pulling rate and a fixed method for keeping the pull vertical. That would reduce variation between trials and make the procedure much easier for another student to reproduce reliably.

What exact condition counts as “floating” here? The student should think about whether partial submersion, trapped air, or surface wetting could change the force needed for detachment.

This safety note is too general to count as a meaningful methodological consideration. The student should focus on controls that affect the validity of the data, such as disk wetting, water depth, container size, or balance calibration.

The definition is useful, but it still leaves ambiguity about exactly when the initial reading was taken. The student should state whether the disk was fully settled before reading and whether the string angle or tension changed between trials.

Including the instrument uncertainty is a strong data-recording habit because it shows the student is thinking about precision from the start. This also creates a basis for later uncertainty propagation.

The raw-data table is well structured: radius, repeated trials, and initial/final balance readings are all recorded together. That makes the measurement process traceable and gives the reader access to the evidence behind the processed values.

This formula is incomplete as written and risks confusing the processing step. The student should write the subtraction clearly as $F_{\max }=F_{\text{final}}-F_{\text{initial}}$ so the calculation matches the text and can be checked unambiguously.

The uncertainty reasoning is partially correct, but the student later states a different balance uncertainty elsewhere, which weakens precision. The student should keep the instrument uncertainty consistent and explain whether the propagated value comes from two independent readings being subtracted.

Consider adding a short line showing one full worked example from raw readings to processed force, area, and uncertainty in the same format used in the table. That would make the processing easier to audit and help the reader follow the calculations step by step.

The unit for area is incorrect here. The student should use ${\text{cm}}^2$ rather than ${\text{cm}}^3$, because a wrong unit reduces confidence in the processing even when the numerical values are reasonable.

The interpretation of uncertainty is too broad here. The student should comment on whether 13% is acceptable for the smallest-force trials and whether the uncertainty changes with area could affect the apparent linear trend.

The graph shows a very strong relationship in the plotted data, and the student uses a fitted model to quantify it. That is a useful step because it moves the analysis beyond simply describing the trend qualitatively.

How does the non-zero negative intercept on the graph affect the claim that force is proportional to area? The student should think about whether the fitted model actually supports direct proportionality or only a trend across the measured range.

The conclusion overstates the statistical meaning of the graph. A high $R^2$ shows a strong fit, but the student still needs to address the intercept and the theoretical model before concluding that the relationship is truly linear and proportional.

If the equation contains both a perimeter term and an area term, which term is actually driving the force more strongly in this experiment? Thinking this through would help the student avoid presenting a partial interpretation as a full theoretical match.

The conclusion directly returns to the research question and begins by summarizing the overall answer. That is effective because it keeps the conclusion focused on the investigation’s purpose rather than drifting into unrelated description.

Consider comparing the measured slope with a prediction from the cited model. Even a brief numerical comparison would make the conclusion much more scientific because it would show whether the data agree with theory in magnitude, not just in general direction.

This sentence is well supported by the processed data trend and gives a clear summary of the observed pattern. The student is doing the right thing by linking the conclusion to the evidence rather than making a purely narrative statement.

The scientific-context comparison is not yet convincing because it treats a perimeter-based equation as if it confirms an area-based conclusion. The student should explain the variables in the model and show quantitatively how the theory relates to the measured gradient.

This conclusion is stronger than the evidence justifies. The student should qualify the claim because the graph also shows a negative intercept and the theory section suggests a perimeter-based dependence, so the result is not fully established as direct proportionality.

This is an overstatement of the cited literature. The equation actually points to perimeter as the direct geometric factor, so the student needs to distinguish carefully between dependence on radius, perimeter, and area.

This is too generic and not well supported by the tables or graph. The student should identify what specific feature of the data shows the trend and avoid claiming “no outliers” unless the dataset has been checked against a clear criterion.

The evaluation opens with a balanced framing of the investigation and prepares the reader for a critical discussion. That is a good sign of reflective thinking because it avoids treating the experiment as flawless.

Multiple trials reduce the impact of random error, but they do not prevent it entirely. The student should revise this statement to show a more accurate understanding of uncertainty and explain how repeats improve reliability rather than eliminate error.

Precision and systematic error are not the same thing. The student should explain which specific systematic errors were reduced by the equipment and which remained, because “precise” equipment alone does not guarantee freedom from bias.

The improvement is practical and directly connected to the identified weakness. The student is not just naming a fix; they are linking material rigidity to the quality of the detachment measurement.

This limitation is specific and plausible, and the student explains why it could alter the measurements. That makes the evaluation more convincing than a generic list of errors because it shows an understanding of how method affects data quality.

Consider stating a target temperature range and how it would be maintained, not just measured. That would make the improvement stronger because it would show how the student would actually reduce variation across trials.

A better improvement would be to use one calibrated balance for all trials, or calibrate every balance against the same standard before use. That would control for instrument-to-instrument variation more effectively than simply rearranging which balance measures which disk size.

The explanation is useful, but the impact is overstated and could be more carefully reasoned. The student should distinguish between zero offset and random uncertainty, because a non-zero start does not automatically double the uncertainty unless that is justified by the instrument behaviour.