IB Environmental systems and societies (ESS) - Old SL Internal Assessment Example

The research question is focused and measurable: it has a clear location, time span, and variables. Including wildfire area burned, PM2.5, and asthma prevalence makes the investigation specific enough to support meaningful analysis.

The context is relevant, but the introduction still treats the issue mainly as a simple wildfire-to-asthma pathway. To fully frame the environmental issue, the student should connect wildfire smoke to broader system factors such as drought, meteorology, and other urban air pollutants that also affect California air quality.

The personal connection adds motivation, but the student should keep it brief and let evidence carry the paragraph. Replacing some autobiographical detail with a source-based explanation would strengthen objectivity and maintain a more scientific tone.

Consider adding one short paragraph that explains why California is a particularly suitable case study. A brief link to fire-prone vegetation, drought conditions, and population exposure would make the issue context more convincing.

If PM2.5 is central to the research question, what evidence would be needed to show that it mediates the wildfire–asthma relationship rather than simply co-occurring with it?

The supporting figure is a strong choice because it links PM2.5 exposure to health outcomes using quantified evidence. This helps justify why PM2.5 is an important variable rather than leaving the rationale purely theoretical.

The hypothesis is understandable, but it still implies a direct causal chain without clearly separating the wildfire–PM2.5 relationship from the PM2.5–asthma relationship. The student should state the expected direction for each link more precisely so the hypothesis matches the later analysis.

The sampling strategy is described, but the justification is incomplete. The student should explain why annual statewide averages are valid for inference about health impacts and address the risk of ecological fallacy caused by aggregating very different regions into one dataset.

The method is clear enough to replicate: the student identifies the data sources, the time frame, and the processing steps used to prepare the variables. This transparency is a real strength because it allows another researcher to follow the same procedure.

The method is repeatable, but it is not yet fully appropriate for the research question’s “contributing” claim. A simple correlation design cannot test mediation or control for confounding factors, so the student should revise the design or narrow the question to match the analysis actually being performed.

The planning section does not build in assumption checks for Pearson’s correlation. The student should state how linearity, outliers, and time-series autocorrelation will be checked, because without that the validity of the statistical method is less secure.

The student could strengthen the planning by adding an explicit justification for the time-series window and for using secondary data rather than primary surveys. That would show why the chosen sampling approach is the best available option, not just a convenient one.

The ethics paragraph is a good start because it recognises the responsibility to use accurate secondary data when discussing public-health implications. That awareness of possible real-world consequences shows thoughtful planning.

The ethical discussion remains too general. It should also address data integrity, source bias, and the risk of overinterpreting correlated state-level data as individual health causation, especially when the findings might be used to inform policy.

The raw data table is appropriate because it presents the underlying values before the statistical analysis. This makes the later correlation results easier to verify and shows a clear data trail.

Including the correlation formula is helpful because it shows how the statistic was obtained rather than treating the result as a black box. That supports transparency in the analysis process.

The interpretation of $r=0.219$ is correct but incomplete. The student should also explain that the relationship is weak and non-significant, so it does not provide strong evidence that wildfire area burned and PM2.5 move together in a meaningful way.

The calculator explanation interrupts the flow of the analysis and reads more like working notes than analytical writing. The student should replace this with a brief sentence stating the inputs and the result, then move directly to interpretation.

This is a useful interpretation, but it would be stronger if the student also stated the practical meaning of the correlation. For example, does this relationship suggest a small, moderate, or potentially misleading association when considered alongside the non-significant PM2.5 findings?

If PM2.5 is not significantly associated with asthma in the dataset, what does that imply about the original research question and the strength of the proposed pathway?

The PM2.5–asthma result is reported clearly and accurately as weak, negative, and not statistically significant. That honesty is important because it prevents the conclusion from overstating the evidence.

The conclusion does recognise both the significant wildfire–asthma relationship and the non-significant PM2.5 result. That balanced reporting improves credibility because it reflects the data rather than only the preferred outcome.

The analysis still leaves the research question unresolved because the PM2.5 relationship does not support the proposed mechanism. The student should address this mismatch directly and explain whether the hypothesis should be revised, rather than treating PM2.5 as only a secondary detail.

The conclusion evaluation is still too narrow in environmental terms. The student should explain what the pattern means for air-quality management, wildfire preparedness, and vulnerable populations, not only for medication planning.

The evaluation begins by linking the findings to public-health relevance, which is a sensible starting point for environmental interpretation. The student is correctly moving beyond raw numbers toward real-world implications.

The method evaluation would be stronger if it addressed validity risks such as outliers, changing diagnostic practices, and autocorrelation in annual data. These issues matter because they can alter the apparent strength of a correlation across time.

The discussion of the longitudinal dataset is a genuine strength because it explains why the evidence spans a meaningful time period. That gives the evaluation a solid basis for judging reliability and trend coverage.

A stronger extension would be to specify exactly how regional sampling or additional PM2.5 sources would be combined and then analysed. Concrete implementation steps would make the improvement more than a general idea.

The improvements are meaningful, but they do not yet address the biggest weakness: the mismatch between the research question and the analysis. The student should propose a more targeted redesign, such as multivariate analysis or a revised question that does not require mediation testing.

How would the evaluation change if the student compared this statewide pattern with data from a smaller region where wildfire smoke exposure is more uniform?

The overall structure is clear and easy to follow, with conventional IA sections presented in a logical sequence. This organisation helps the reader locate the research question, analysis, evaluation, and application quickly.

The variables section contains a terminology error: PM2.5 is not a measure of current asthma rates, and the dependent variable should be stated separately from the explanatory variable. The student should rewrite the variables so each one is defined accurately and unambiguously.

The description of PM10 is inaccurate here: PM10 refers to particles up to 10 micrometres, not centimetres. This matters because precision in scientific terminology affects the credibility of the whole analysis.

Some parts are wordy and repetitive, especially where calculator instructions are included in full. The student should condense these passages so the argument stays focused on the results rather than the tool used to obtain them.

Reporting the p-value directly is concise and informative. This makes the statistical decision easy to verify without forcing the reader to infer significance from the graph alone.

The student could improve coherence by reducing repeated explanatory sentences and moving directly from the statistic to its significance for the research question. A more compact explanation would make the argument easier to follow.

Would the argument be clearer if the student grouped all alternative pollutants together in one evaluation paragraph rather than introducing them separately at the end of the analysis?