IB Physics HL Internal Assessment Example

The research question is focused and clearly identifies the independent and dependent variables. This makes the investigation directionally strong because the reader can immediately see what is being tested.

The student opens with a clear real-world observation that gives the investigation a specific context. Linking the research question to tennis-court maintenance helps justify why the variable matters, rather than presenting it as an isolated laboratory exercise.

Consider tightening the wording further by specifying the ball type, drop height, and surface material in the question itself. That would make the scope even more precise and would reduce ambiguity about the exact conditions being investigated.

The hypothesis is too vague to be fully testable as written. The student should state the expected direction more explicitly, such as predicting that rebound height decreases as thickness increases, so the claim can be directly evaluated against the results.

The hypothesis is clearly linked to the variables being studied and shows an expectation of a trend before data collection. That is useful because it establishes a testable direction for the analysis, even if the final functional form is still open.

The measurement method is detailed enough to support replication, and the use of 240 fps video is a practical way to capture a fast bounce. Stating exactly what part of the ball was tracked also improves methodological clarity.

This justification is not fully convincing because it treats a calculated contribution as negligible without showing the threshold used to make that decision. The student should explain the criterion for omission, or include the term if it is part of the uncertainty budget.

The procedure still leaves an important ambiguity: it is not fully clear whether rebound heights are measured from the container base, the sand surface, or another reference point. Because the sand height changes during the investigation, the reference level must be defined precisely for the method to be reproducible.

The student should explicitly state how this fixed table height is used in the height calculation. Clarifying the measurement datum would make the method more reproducible and would also help prevent confusion later when the sand layer thickness changes.

Repeating each condition five times is a sensible design choice because it reduces the influence of single outliers and allows a mean and spread to be calculated. This strengthens the reliability of the dataset.

The procedure does not address the key issue that adding sand changes the surface height, not just the thickness. The student should specify whether the drop distance is kept constant relative to the sand surface or corrected after each thickness change.

This is a strong control strategy, but the student should also explain how the randomisation was implemented in practice. A brief description of the random sequence generation would improve reproducibility.

This is a valid uncertainty to identify, but it is treated too confidently as if its effect were fixed and easily corrected. The student should explain whether the parallax shift changes with rebound height and why a single value can represent all trials.

The parallax calculation is difficult to trust because the setup details are not used cleanly to show how the final uncertainty was obtained. The student should lay out the geometry step by step so the conversion from camera position to height error can be checked.

The raw-data table is organized clearly by thickness and trial number, which makes it easy to track the dataset before processing. That structure is helpful because it lets the reader verify the mean and spread calculations from the original measurements.

The student explains why standard deviation is an appropriate choice for repeated measurements, which shows awareness of the statistical basis of the processing. That makes the uncertainty treatment more transparent and more scientifically grounded.

This gradient is inconsistent with the data shown on the graph and the stated drop from about 69.3 cm to 43.1 cm over 2.4 cm. The student should recalculate the slope carefully, because the current equation appears to be off by a factor of ten.

The interpretation of $R^2$ is clearly linked to the plotted trend and helps the reader understand why the linear model was chosen. This shows effective communication of processed data and its statistical meaning.

The decision to omit the lower error bar at $d=0$ is understandable, but the analysis should explain how this affects comparability with the other points. A one-sided uncertainty or a note on asymmetric error bars would communicate the measurement more precisely.

This is a serious processing error because the symbol $m$ is being used as though it were a slope, but the expression still includes $d$ like an equation. The student should write the gradient as a single numerical value with units, not as a full line equation.

The conclusion about random error is too broad because it relies on a flawed gradient calculation. The student should first verify the processing, then distinguish whether the dominant source is random scatter, systematic offset, or both.

The coefficient of restitution calculation is relevant, but the analysis does not address the changing sand-surface height, which affects the true drop height $h_i$. Without correcting the datum, the calculated $e$ values may be biased even if the arithmetic is correct.

Consider adding a short worked example for one intermediate thickness as well as $d=0$ cm. That would let the reader check whether the same method was applied consistently across the full table, not just for the first row.

What would change in this claim if the professional court and the container setup were compared on compaction, substrate depth, and ball testing protocol rather than only on the reported $e$ values? Considering those factors would make the conclusion more scientifically cautious.

The comparison is numerically useful, but it is not evaluated against the uncertainty of the external value. The student should consider whether a 0.03 difference is meaningful once measurement uncertainty and differences in test conditions are taken into account.

The student supports the conclusion with specific numerical evidence, which strengthens the chain from data to claim. Citing both endpoints and the percentage change makes the conclusion more persuasive than a purely qualitative summary.

The student should qualify this practical implication more carefully. A narrower claim, such as saying the trend suggests possible surface tuning under the tested conditions, would remain faithful to the evidence without overextending it to full court optimization.

The conclusion answers the research question directly and stays aligned with the main pattern in the data. This is effective because it does not drift into unrelated claims and keeps the focus on the measured relationship.

This conclusion is not fully consistent with the analysis because the stated line of best fit contains the same factor-of-ten error as the processing section. The student should revise the equation before relying on it as evidence for the claim.

The student identifies a specific methodological weakness and explains how it affects the impact conditions and rebound height. This is stronger than a generic limitation because it links the source of error to the physics of the collision.

The suggested improvement is realistic, but it is too general to fully explain how it would improve the data. The student should specify the device or method, such as an रिलीज gate or clamp release, and explain how it would standardise launch angle and velocity.

This quantified claim is not well supported because the analysis does not convincingly show how parallax accounts for that proportion of variance. The student should either justify the estimate with calculations or present it as a qualitative limitation instead of a precise percentage.

This strength could be developed into a stronger evaluation by comparing the experiment more explicitly with professional court construction and testing conditions. That would help show not just that the material is relevant, but why the setup only partially models real courts.

The student recognises that sand arrangement is an unavoidable source of trial-to-trial variation, which shows thoughtful evaluation of the experimental system. That is useful because it demonstrates awareness that not all limitations can be eliminated completely.

This improvement is relevant, but it is incomplete because it stops before stating how the visibility problem would be solved. The student should also suggest a calibrated side-on setup or a fixed optical alignment to reduce parallax without sacrificing measurement readability.