IB Physics SL Extended Essay Example

This research question is quantitatively framed and focused on measurable variables. Naming both the composition change and the dependent variable gives the investigation a clear direction and supports coherent analysis later.

Consider stating the composition as a sand:cement ratio rather than only “increasing sand mass relative to cement mass.” That would make the independent variable even more precise and easier to compare across the five mixtures.

The title is clear and specific, immediately signalling the material system and the dependent variable. This helps frame the investigation from the start and makes the report’s purpose easy to follow.

The introduction briefly but clearly outlines the experimental plan, which helps the reader understand what was actually tested before the method is described in detail. That early signposting improves focus and communication.

The wording becomes conceptually imprecise here: the mixture is repeatedly called “concrete,” but the report later states that no further aggregates were used. The student should use one accurate term consistently, because terminology affects scientific clarity and the credibility of the investigation.

The independent variable is identified directly, and the paragraph explains why the chosen mass levels were adjusted after preliminary trials. That is useful because it shows purposeful selection rather than arbitrary data collection.

The student should correct this to “curing process” and explain how curing was standardised. At present, the variable is named but not clearly controlled in a way that lets the reader judge whether the method is sufficiently consistent.

This step claims the system reaches thermal equilibrium, but the report later shows only a fixed heating time and no evidence that steady-state conditions were achieved. The student should either justify this assumption with evidence or revise the method so it matches what the experiment can actually support.

The student shows useful subject awareness by connecting Fourier’s law to real applications. That helps demonstrate that the physics is not being used abstractly, but in a relevant material context.

The symbol $Q$ is being treated as “total heat transferred” while the unit shown is W, which is a unit of power, not energy. The student should distinguish between heat energy in J and heat-transfer rate in W, because this distinction is essential to the physics and the formula’s meaning.

This general statement is reasonable, but it is used to justify keeping the temperature “the same for each block” without fully controlling the actual measurement conditions. The student should be careful to separate the theory from the experimental control, otherwise the justification sounds stronger than the evidence allows.

The hypothesis is plausible and linked to material structure, but the explanation overstates the certainty of “air pockets” as the sole cause. The student should frame this more cautiously and connect it to porosity and density, which are better-supported physical mechanisms.

This assumption is not securely established from the experiment itself, and it becomes the basis for all thermal-conductivity values. The student should justify the power model with direct evidence from the apparatus or, preferably, use a method that measures the heat flow more reliably.

The calculated conductivity values are much higher than the literature values cited later in the report, which suggests the physical interpretation is not secure. The student should discuss this mismatch explicitly when presenting the values, rather than treating them as straightforward measurements.

This conclusion is too confident for the evidence presented. A percentage uncertainty alone does not prove reliability, especially when the dominant uncertainty is likely systematic rather than random. The student should distinguish between precision in the data and validity of the method.

The student shows clear analytical method here by defining the averaging process explicitly. This makes the processing reproducible and helps the reader follow how raw trials were turned into usable data.

The sample calculation is internally inconsistent: the denominator should be $5$, not $16.08$. The student should correct the arithmetic and show the full calculation clearly, because a small processing error here weakens confidence in the later analysis.

The student moves from measured temperatures to conductivity too quickly, without showing why the steady-state conduction model is valid for this setup. The analysis would be stronger if it explained the assumptions behind the calculation and tested whether the apparatus actually satisfies them.

This interpretation is incorrect because the graph’s gradient is positive for temperature change versus sand mass, but the research question concerns thermal conductivity. The student should avoid calling the overall conductivity trend a “positive correlation” when the actual conclusion is that conductivity decreases as sand content increases.

The regression is reported, but the interpretation around it is not rigorous enough. A high $R^2$ does not by itself prove that the model is accurate or that the data are valid; it only shows how well the line fits these points. The student should separate fit quality from experimental correctness.

The argument here conflicts with the trend being discussed: the conductivity values decrease as sand content increases, so the relationship is negative, not positive. The student should revise the statistical language so that the direction of the correlation matches the actual data and the claim being made.

If the same systematic issue affected every block, what evidence shows that the error was truly constant across all compositions rather than composition-dependent? The student should think carefully about whether contact area, moisture, or porosity could change the trend itself, not just the absolute values.

This is a strong evaluation point because it links a limitation directly to a practical improvement. The suggestion is also method-specific, showing that the student understands why power loss affected the results and how to reduce it.

The conclusion overstates reliability because it relies on small instrument-based uncertainties while ignoring the dominant systematic uncertainty identified elsewhere. The student should make the conclusion more cautious and explicitly distinguish between the observed trend and the validity of the absolute conductivity values.

The report follows a conventional investigation structure, and the contents page helps the reader navigate it efficiently. That organisation supports clarity and makes the sequence of sections easy to track.

The inclusion of a table for the independent variable improves readability and makes the composition levels easy to compare. A structured presentation of the sample sets is especially helpful in a data-heavy investigation.

Consider adding a brief note in the caption or directly beneath the chart to explain the source’s ratio system and how it compares with the primary samples. That would make the secondary comparison easier to interpret without interrupting the flow of the discussion.

The bibliography entry is not consistently formatted, and several sources are merged into one line. The student should standardise the citation style and separate each reference clearly, because a clean bibliography strengthens academic credibility and makes sources easier to verify.