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Crafting a Chemistry Internal Assessment can be daunting, especially aligning it with the official grading criteria. However, many pitfalls can be sidestepped during the design, writing, or review stages. From refining research questions to enhancing data analysis, this guide aims to steer you clear of these challenges, ensuring you achieve top marks for your IA.
Top 10 mistakes in Chemistry IA
Poor data analysis: Identifying significant relationships in your results requires thorough data analysis. Present your process comprehensively, including calculations, processed data tables, and preferably, results of statistical analysis.
To see how it should look like, check this example.
Poor Communication: Criterion E, Communication, focuses on work presentation. Before submission, ensure consistent page numbering, text justification, and labeling of figures and tables—maintain a uniform format: a number and a brief description of contents.
Properly executed Communication can be found here.
Incomplete evaluation: A comprehensive evaluation should discuss study strengths, weaknesses, and analyze potential impacts of different error sources. Categorize errors accordingly—Chemistry recognizes systematic and random errors.
To see this in details, check this one.
Not including enough trials: Regardless of your experiment's design, repetition is essential. Averaging results from multiple trials enhances reliability and minimizes the impact of random errors or outliers. While some suggest at least 3 trials, in most cases, 5 trials are ideal.
To see what we're talking about, see this exemplar.
Too large (or too small) background info section: Background information should enhance understanding of investigation elements—design, results, and discussion. Ensure the introduction covers all research question elements while omitting irrelevant information.
Short yet perfectly written background information here.
Neglecting uncertainty consideration: Every data collection process carries method uncertainty, often linked to equipment uncertainty. Analyze its impact by calculating percentage uncertainty relative to results and address its impact on reliability.
And a perfect example of a proper way to do it in this IA.
Not including qualitative data: Criterion C, Analysis, requires the inclusion of both quantitative and qualitative data. Describe changes in color, temperature, or smell of experimental solutions for maximum marks.
Check good data presentation here.
Unfocused research question: Many students include a research question, often incomplete. A correct one specifies independent and dependent variables, measurement method, unit, and literature name of the studied reaction.
Great RQ is formulated in this exemplar.
Lack of reference to the results in the conclusion: A relevant conclusion directly follows from results. Demonstrate the link by quoting specific figures from tables or addressing observed trends on graphs.
To see what this is all about, check this IA.
- Not labeling the sources of error: Including errors isn't enough - classify them properly. In Chemistry IA, categorize errors into systematic (equipment and experiment design limitations) and random errors (observer perception defects, like incorrect titration meniscus reading).
And lastly, this one shows it well.
We hope you found this post helpful. For more useful materials associated with the IB, check out the wide variety of IA, EE and TOK exemplars available at Clastify.
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