IB Physics SL Internal Assessment Example

The research question is focused and measurable: it clearly identifies the independent variable, the dependent variable, and the experimental object. That precision makes the investigation reproducible and helps keep the analysis tightly linked to the question.

This explanation is not physically secure for a ball dropped vertically onto the ramp. The student should distinguish between motion along the plane before impact and the collision itself, otherwise the theoretical context becomes inaccurate and weakens the foundation of the investigation.

The background successfully situates the investigation in a real physics context and shows why the question matters. This gives the study an appropriate scope and helps justify the chosen variables.

The model relies on strong idealisations that are not justified for the real ping-pong collision being studied. The student should state clearly where these assumptions are approximate and how likely departures from them might affect the investigation.

The derivation is incomplete here, and the omitted step matters because the time of flight depends on the launch height above the floor, which changes with incline. The student should derive this term explicitly from the geometry of the setup rather than assuming the same height for every angle.

The student could strengthen the introduction by stating the model’s assumptions before deriving predictions, such as the launch point, launch angle, and whether the rebound height above the floor changes with $\theta$. That would make the theory easier to evaluate against the actual setup.

The linearized model is not sufficiently justified from the geometry shown in the experiment. The student should re-derive the projectile motion using the actual launch height after impact; otherwise the later graph of $R$ against $\sin (2\theta )$ is built on an oversimplified equation.

Using six angle values gives the investigation enough breadth to reveal an overall trend rather than a single-point effect. That choice supports a more meaningful analysis than testing only a few isolated inclines.

The student correctly recognises that repetition improves reliability and reduces the influence of random scatter. This is a good methodological habit that should be continued whenever a measurement is likely to vary from trial to trial.

This control is helpful, but the student does not explain how the release height is measured reproducibly or how the ball is released without an added push or spin. A repeatable release mechanism would make the method much more credible.

Consider stating exactly how the first impact point is chosen when the salt mark is irregular or spread out. A brief rule, such as using the centre of the first visible mark, would improve reproducibility.

This step gives a basic angle-setting procedure, but it does not explain how the angle is checked after each trial or how the plank is prevented from shifting. The investigation would be easier to reproduce if those controls were specified.

The raw data are presented clearly and systematically, with all five trials shown for each angle. This makes it easy to verify the processed values and follow the progression from raw measurement to analysis.

This treats half-range as the only uncertainty source, but it does not distinguish spread in repeated trials from instrument uncertainty. The student should show how these are combined, rather than assuming that half-range alone fully represents the measurement uncertainty.

The repeated values are internally consistent enough to show a clear pattern across trials, which supports the later calculation of means. That consistency strengthens the reliability of the recorded data.

The method for uncertainty calculation is not written clearly enough to be checked. The student should present the formula cleanly and define whether the reported value is a random spread, an instrumental uncertainty, or a combined uncertainty.

The mean values appear to be calculated correctly from the repeated trials, which shows competent processing of the raw measurements. Accurate averaging is essential because it gives the graphs a trustworthy central trend.

This is too vague for a serious uncertainty analysis because the actual propagation method is not explained. The student should show the derivation or calculation steps that lead from angle uncertainty to uncertainty in $\sin (2\theta )$.

The propagated uncertainty in $\sin (2\theta )$ is handled with appropriate numerical sensitivity. That is a strong step because it shows the student is trying to quantify uncertainty rather than ignoring it.

The graph is described as quadratic, but the analysis does not show a regression equation, fit parameters, or goodness-of-fit measure. The student should include the actual fit so the claim of a quadratic relationship can be evaluated quantitatively.

What evidence in the processed data most strongly supports this statement: the shape of the angle-range graph, the linearized graph, or the uncertainty values? Thinking about which evidence is most persuasive would help the student distinguish pattern from proof.

This justification is not strong enough to remove the error bars entirely. The student should either rescale the axes or present the uncertainties in a way that still allows the reader to judge significance, because uncertainty information should not be discarded for visual convenience.

This claim overstates what the graph alone can prove. A curved plot suggests deviation from linearity, but without a fit, residuals, or comparison to uncertainty, the student cannot claim confirmation that the model is fundamentally wrong.

This conclusion is too strong because removing error bars does not prove the cause of the deviation. The student should distinguish between a visible trend and a causal explanation supported by evidence.

This is a reasonable summary of the observed pattern, but the final statement still overreaches on causation. The student should keep the conclusion tightly tied to what the experiment directly measured and what it can only suggest.

This appears to be a model statement written incorrectly, and it weakens the precision of the conclusion. The student should restate the intended equation accurately and then compare the results directly to that corrected model.

This is a plausible explanation, but it is not directly demonstrated by the data. The student should avoid presenting inferred collision mechanisms as established facts unless they have been measured or supported with a more accurate model.

The conclusion is well supported by specific processed values, which makes the final claim easy to trace back to the evidence. That numerical linkage is exactly what strengthens a conclusion in a physics investigation.

The null hypothesis is rejected without statistical testing, so the wording is too confident. The student should either justify rejection with an appropriate test or soften the claim to reflect the descriptive nature of the analysis.

The conclusion would be stronger if it briefly explained why the peak occurs near $30^{\circ }$ in terms of the measured setup rather than general intuition. That would make the final interpretation more scientifically grounded.

The student does a good job of linking the conclusion back to the linearized analysis rather than relying only on the raw graph. That connection shows awareness of how the model was being tested.

The student identifies a concrete limitation and ties it directly to a specific uncertainty source. That is effective evaluation because it shows the limitation is not generic but linked to the actual method used.

Which evidence in the data demonstrates that the angle uncertainty is the dominant source of non-linearity rather than one contributor among several? Reflecting on that distinction would help the student make the evaluation more precise.

This limitation is specific and physically relevant because it names the processes that likely altered the rebound. The student is showing good awareness of how the method could have influenced the results.

This improvement is realistic and well matched to the problem. The student could make it even stronger by explaining how often the angle should be rechecked during the experiment to prevent drift across trials.

The student goes beyond listing a limitation by explaining its effect on the analysis. That connection between method and consequence is exactly what makes evaluation strong.

The limitation is identified, but the explanation stops short of showing how much it affected the results compared with other sources of error. The student should comment on relative impact, not just existence, to reach a higher evaluation level.

This is a strong improvement because it would convert a qualitative limitation into measurable data. The student should consider stating exactly which quantities would be extracted from the video so the benefit is even clearer.

The student identifies a very specific weakness in the distance-measurement method and explains why it matters. That specificity helps the evaluation feel evidence-based rather than generic.

The student states the scatter, but does not explain why those angles were more affected than the others. The evaluation would be stronger if it linked this pattern to the actual conditions of the trials or to the visual evidence in the data table.

This is a realistic and directly relevant improvement because it targets the measurement problem at its source. The student could strengthen it further by comparing which option best balances precision, cost, and ease of use.

The final synthesis is strong because it connects the proposed improvements back to the model, the data, and the overall reliability of the investigation. This shows clear understanding of why methodological refinement matters.