IB Physics HL Internal Assessment Example

The dependent variable is named as “wave amplitude,” but the investigation later records Audacity FFT values in decibels. The student should align the wording of the question with the actual measured quantity, because amplitude, FFT magnitude, and dB are not interchangeable terms.

The research question is impressively focused and quantitatively specific. By naming five thicknesses, eight harmonics, and the E4 string, the student creates a narrow enough investigation to be tested empirically rather than remaining purely descriptive.

This explanation is useful, but the relationship between the FFT output and the reported “Sound Pressure Level” still needs more precision. The student should clarify whether the software gives relative digital magnitude or calibrated sound pressure, because that affects the validity of the dependent variable.

This conflicts with the research question, which studies the high E string, and with the later procedure that uses the E4 string. The student should make the target string consistent throughout the report, because switching strings changes the fundamental frequency and invalidates direct comparison.

The independent variable is operationalized clearly enough for the investigation to be reproduced. Listing discrete thickness values gives the reader a concrete experimental range rather than a vague description of “thin” versus “thick” picks.

The method is not internally consistent: this step refers to the 6th string, while the rest of the investigation is framed around the first/high E string. The student needs to standardize the string reference and explain the sticker placement in relation to that same string.

What would happen to the recorded harmonic pattern if the microphone distance stayed fixed but the amplifier gain changed? Thinking about this would help the student separate true plectrum effects from recording-system effects.

The procedure includes repeats, which is good, but it still does not fully specify how trials were selected, rejected, or labeled. Because later processing depends on matching each trial to a thickness and harmonic, the student should describe a clearer trial-recording system.

A short justification of why these specific plucking conditions were chosen would strengthen the design. Explaining how $5\mathrm{N}$ and the $4\mathrm{cm}$ displacement were selected would show more deliberate control of variables rather than only procedural control.

The raw-data section makes the processing pathway traceable from recording to FFT output. This is strong communication because the reader can see exactly where the numerical values in later tables came from.

The unit label is not sufficiently precise for the analysis that follows. The student should distinguish between calibrated sound pressure level and the relative decibel scale produced by the audio software, because the interpretation of the values depends on that distinction.

A separate table for each plectrum thickness is an effective organizational choice. It keeps the repeated measurements readable and makes cross-comparison between thicknesses much easier.

This sentence is unclear and does not explain how the uncertainty was actually determined. The student should state the exact rule used for uncertainty assignment, because “dispersion” alone is not enough to justify the error bars.

Several values in the processed table appear inconsistent with the raw data, so the student should verify the arithmetic before relying on the mean table. Accurate processing is essential because any later trend line is only as valid as the numbers entered into it.

The student states the mean calculation clearly, but the wording is mathematically incomplete because averaging requires summing the five values before dividing by 5. The processing step should be written explicitly so another reader can reproduce it without guesswork.

Using a consistent colour scheme is a strong communication choice. It helps the reader track the same harmonic across the table and graphs without needing to re-check labels repeatedly.

The analysis relies heavily on $R^2$, but it does not test whether a linear model is the best choice or whether the slope differences are statistically meaningful. The student should go beyond goodness-of-fit and examine uncertainty in the gradients themselves.

Dividing the data into two groups is sensible, but the student does not explain why the split occurs at the 5th/6th harmonic. A stronger analysis would justify the threshold using the data rather than treating the division as self-evident.

The student correctly notices that the higher harmonics behave differently from the lower ones. Identifying this change in trend is a useful analytical step because it shows the data were not forced into one oversimplified pattern.

If the sign change near the 6th harmonic is central to the conclusion, what physical mechanism could explain why the effect reverses there? Considering that mechanism would make the conclusion more scientifically grounded.

This claim is not demonstrated mathematically. The student should show how the trend lines were compared with the uncertainty ranges, because simply stating that they “lay within” them does not prove that the trend is statistically secure.

The conclusion stays focused on the stated research question and returns to the key variables. This is effective because it shows the student is answering the investigation rather than drifting into a general summary.

It is strong that the student acknowledges the size of the uncertainty and relates it to the measured value. This shows some awareness that significance depends on scale, not just on whether an error bar exists.

This conclusion is too strong for the evidence presented. The results suggest a trend, but they do not prove that the methodology was optimal or that all effects are attributable only to plectrum thickness, given the uncontrolled variables and uncalibrated audio measurements.

The recommendation is unclear because it is not obvious what “they” refers to. The student should specify which uncertainty source or variable should be reduced, and how doing so would improve confidence in the conclusion.

A high $R^2$ does not by itself establish that the relationship is physically strong or meaningful. The student should avoid treating fit quality as proof of causation and instead connect the trend back to the underlying wave behavior.

Although the wording is imperfect, the improvement is clearly targeted at a real limitation. Replacing the laptop microphone with a better measurement device would reduce one of the main sources of distortion in the spectral data.

A stronger suggestion would name the exact equipment and the exact uncertainty it would reduce. For example, specifying a calibrated microphone and fixed amplifier gain would make the improvement operational rather than general.

This weakness is valid, but it is still quite generic. The student should explain how microphone quality specifically affects the harmonic distribution, for example by changing sensitivity across frequencies rather than simply making the recording “worse.”

This is an important limitation, but the impact is stated too broadly. The student should specify whether the thickness uncertainty mainly weakens the slope, the separation between neighbouring thickness groups, or the confidence in the reversal around the 6th harmonic.

This is a relevant and specific strength because it targets a real source of variation in manual plucking. The comment shows awareness that the quality of the data depends on controlling the input conditions, not just on measuring outputs.

This limitation is relevant, and it should be ranked as one of the most serious because it directly changes the excitation of the string. The student should explain that these uncontrolled plucking variables likely affect the harmonic spectrum more strongly than small thickness differences do.

The student correctly identifies repeat trials as a strength, and the justification is sensible. Repetition helps reduce random error and makes the mean values more dependable.

To strengthen this improvement, the student should explain how the mechanical arm would be standardized — for example, fixed release angle, displacement, and plucking force. That would make the link between the weakness and the solution much more convincing.

This is a reasonable extension, but it is not yet framed as an evaluation of the current method. The student should explain whether more harmonics would increase reliability, reveal a threshold effect, or test whether the sign change is sustained beyond the 8th harmonic.