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The process of writing the Biology Internal Assessment can be challenging, especially when trying to fit into the official grading criteria. There is room for making a broad range of mistakes, fortunately, most of them can be avoided either in the design, writing, or review stage of creating your IA. From refining research questions and hypotheses to improving data analysis, this guide aims to help you navigate these pitfalls, ensuring that you score top marks for your Internal Assessment
Top 10 mistakes in Biology IA
Poor data analysis: Data analysis is a necessary element of identifying significant relationships from your results. This involves conducting calculations, which should always be included (even if they involve relatively simple operations like obtaining the mean), as well as presenting the processed data tables and graphs. In the case of Biology IA, it is also necessary to run a statistical test, which should be chosen according to the type of data that you collected.
Here you may find an excellent example of complete data analysis.
Poor Communication: The criterion E, Communication, is mostly concerned with the presentation of the work. Although it might seem like a collection of insignificant details, it still represents about ⅙ of the mark scheme. Before submitting your work, check if your pages are numbered, the table content is centred, and the text is justified.
This is an excellent example of a well-presented IA.
Incomplete evaluation: Most students include some kind of evaluation of their work; however, it is often incomplete. Always remember that a comprehensive evaluation section includes a discussion of both the weaknesses and strengths of the study, as well as an analysis of how different sources of error could have impacted the final results. Sometimes, structuring this section in a table form will improve the clarity of the presentation.
Check this one to see a good evaluation section.
Not including enough trials: Repetitions of the experiment are crucial to ensure that the results are significant and the study is replicable (in other words, your findings are not coincidental and represent a valid relationship between your independent and dependent variables). Although some teachers advise conducting at least 3 trials, in most cases 5 trials will be ideal.
Take a look on this IA to get a more detailed information.
Too large (or too small) background info section: Background information cannot be either limited or irrelevant. Make sure that the introduction addresses all of the elements of your research question, like independent and dependent variables, as well as the studied organism and process. At the same time, omit the information that does not improve the understanding of your experiment, for example, an extensive description of the brewing industry when writing an IA on yeast fermentation.
Here you have a perfect example of properly done Background Information.
Lack of uncertainty consideration: The correct analysis of the work involves not only the identification of major methodological and human errors, but also the consideration of an unavoidable limitation - the uncertainty of the measuring equipment. To ensure that the criterion is fully satisfied, calculate the total percentage uncertainty of the results and evaluate its impact on the reliability of the findings (it might be helpful to compare the obtained value to the percentage error if literature values for the investigated reactions are available).
This is uncertainty consideration you should be looking for.
Stating only the alternative hypothesis: In the case of Biology IA, it is necessary to state both null and alternative hypotheses. The null hypothesis will claim that your independent variable does not impact the dependent variable, whereas the alternative hypothesis is the one that states that there exists a relationship between them. This is necessary, as the statistical tests work by disproving the null hypothesis.
Proper hypothesis in this example.
Unfocused research question: Most students will include a research question in their work, however, it is often incomplete. A correct research question will involve the specification of the independent and dependent variables together with the method and unit of measuring, as well as the name of the species on which the investigation is conducted.
Perfectly executed RQ to be seen here.
Lack of reference to the results in the conclusion: The conclusion must always directly follow from the results of the experiment, hence it needs to directly refer to them. This is best achieved by quoting specific data points, like percentage change, smallest and highest values from the graphs, or results of the statistical analysis.
In this exemplar you can see a well structued conclusion with references.
- Not labeling the sources of error: It is not enough to include errors from the study, they must also be properly classified. In the case of Biology IA, the sources of error should be divided into methodological, which are connected with the limitations of the equipment and the design of the experiment, and human error, reflecting the defects of the observer’s perception (like incorrect reading of the measuring equipment).
See how errors are presented in this IA.
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