IB Chemistry HL Internal Assessment Example

The introduction would benefit from specifying the tea variety used (e.g., black, green) to clarify the scope and improve reproducibility of the study.

The research question is clearly stated and situated within a specific context of tea chemistry and spectrophotometric analysis, demonstrating strong research design and relevance to real-world health implications.

The Beer–Lambert law description uses “I” for path length; standard notation uses “l”. Consider revising to $A=ϵcl$ to align with conventional spectroscopic terminology.

The list of factors affecting tannin levels is useful, but the rationale for buffer choice (pH 4.4 acetate) is not explained. Include justification based on the tannin–metal complex stability.

The stated hypothesis predicts a negative correlation, which contradicts both the literature context and the actual positive trend observed; this misalignment undermines the study’s internal consistency.

The table instructs use of 565Å for measurements but earlier text refers to 540 nm. This inconsistency in wavelength specification could cause procedural errors.

The controlled variables table is detailed with clear significance and control methods, providing a robust foundation for reproducibility.

The procedure section is detailed and logically ordered, giving sufficient information for another student to reproduce key steps.

The steeping procedure lacks precise temperature control parameters (e.g., ±°C tolerance, stirring conditions), which could affect extraction consistency.

The listing of equipment uncertainties is comprehensive for glassware, demonstrating clear communication of measurement precision.

No uncertainties are provided for qualitative equipment like the hot plate or pipette; even an estimated range would aid in evaluating systematic errors.

Include a brief chemical explanation or equation showing how gelatin precipitates tannins to clarify the blank‐treatment mechanism.

The blank‐sample protocol is described clearly with step numbering, supporting transparent data processing.

The raw data table comprehensively records sample and blank absorbances with corrected values, allowing verification of data processing steps.

Uncertainties for each absorbance reading are omitted, and blank‐correction uncertainty is not propagated; include these to meet full uncertainty analysis.

Propagate the ±0.002 absorbance uncertainty through the corrected absorbance and calibration curve to demonstrate its impact on concentration results.

Graph axes lack unit labels and a displayed fit equation with R², which impedes interpretation and precision evaluation.

The dilution‐factor graph appears inverted relative to expected Beer–Lambert behavior; replot to match logical axis convention and clarify trends.

The uncertainty propagation for m includes a term √((ΔD/D)²) but uses inconsistent ΔD value; verify and recalculate each component carefully.

The slope determination is well laid out with sequential equations, enabling transparent calculation of the calibration curve.

The calculation of undiluted absorbance and final concentration omits a clear derivation of units and conversion factors; include dimensional analysis.

The summary data table of tannin concentrations leaves average and uncertainty columns partially blank; ensure all replicate averages and propagated errors are completed.

The conclusion mentions a constant 5–7% percent error without presenting those values in the results; include a summary table or discussion of percent errors.

The conclusion quantifies correlation with a Pearson coefficient, which strengthens the analytic argument and connects to scientific context.

The evaluation effectively links specific methodological weaknesses to improvements and describes their relative impact, demonstrating solid analysis.

The evaluation of strengths could be balanced by critically examining how these positive aspects might still leave residual uncertainties in results.

The evaluation’s strengths section reflects well on the real‐world relevance of the procedure and reagent minimization, showing thoughtful reflection.

Quantify how a constant‐temperature water bath would reduce temperature variability (e.g., target ±0.5 °C) to illustrate impact on systematic error.

Future scope insight is strong; enhance by proposing sample‐size calculations or statistical tests to ensure sufficient power for comparing tea types.