I.A.

Analysis and Approaches SL

Criterion A: (Presentation)

This criterion is all about the looks of your investigation, how well its parts are connected to each other, its flow and generally how easily is followed by peers' audience.

The keywords students should pay attention at are the following:

Organization: You can demonstrate organization by having an introduction, a main body and conclusion.

Coherence: It is when all parts of your investigation link to each other in a natural order to meet your aim.

Concise: It is when you move straight to the purpose of your IA without presenting any parts where the flow is lost.

Action plan

Starting your IA, make sure you provide the reader with a nice introduction in which, if possible, you will be giving a rationale (don't do it superficial) of why the topic was chosen. Your aim must be clearly stated. One common mistake students tend to do is having an aim that is rather vague. Having an "agenda" of aims is not helpful. Better stick it to one or at most two aims both explicitly defined. Describing the process that will be followed is not advised as it might lead to losing conciseness in your paper.

Moving forward, make sure that all parts of your exploration are connected to each other in a nice flow and it doesn't seem as if your are answering questions. The math used throughout your IA must be relevant in the direction of your aim. Having irrelevant math might damage your coherence.

It is always a plus when you provide the reader with some background information regarding your content. For example, if you are working with GDP per capita make sure you spend one or two lines explaining what this is. Always remember that your audience is not an experienced teacher but an average IB student. Your exploration must be easily followed by them. Neither your teacher nor the examiner.

Don't overdo it though. Overexplaining can damage your flow and therefore your conciseness. Especially, don't overdo it when it comes to explaining math that are already parts of your Syllabus.

For example there is no reason explaining what the arithmetic mean is or how the Pearson's correlation coefficient is classified based on its value.

Keep in mind that if the examiner recognizes some organization OR some coherence you will surely get at least 2 out of 4, while if both organization AND coherence are shown your grade will move to 3 out of 4.

One of the hardest things in the IA is getting a 4 out of 4 in the presentation and this is because there are so many tricky ways to lose conciseness. Here is a list of do's and don'ts towards this direction:

Avoid long tables that would make the reader scroll down to continue reading. Better place them in the Appendix and keep just a part of it in your exploration's body

All figures and diagrams must be properly placed to support the context.

Tables should not break into more than one pages. In case that happens, you should put column titles in each one of the succeeding pages.

Avoid repeating the same processes, even if they have to do with different datasets.

Although 12 to 20 pages is rather a recommendation than an obligation, avoid writing an IA longer than that if possible.

Conclusion

By managing to keep a reasonable structure that includes an introduction, a main body, a conclusion, a clear aim and math that are relevant to your aim, you can score an easy 3.

1 Choose a topic that you are passionate about:

Choosing a topic that you are genuinely interested in, will allow you to approach the exploration with enthusiasm and creativity.

For example, if you enjoy sports, you could investigate the correlation between athletic ability and academic performance.

2 Use a variety of statistical methods:

Explorations that get the higher levels in this criterion, often use more than one statistical method. By using a variety of methods, you demonstrate a deeper understanding of statistics and its applications.

For example, you could use regression analysis, chi-square tests, or confidence intervals to analyze your data.

3 Choose a topic that requires you to collect your own data:

While it may be tempting to use existing second had data, using your own, will allow you to demonstrate independence and creativity.

For example, assuming that you wish to investigate the relationship between study habits and academic performance, you could survey a group of students (perhaps your peers) to determine their study habits (hours of studying per day, use of study aids like flashcards or notes) and correlate these habits with their grades.

4 Show a good understanding of statistics outside the syllabus:

To achieve the highest mark, you can demonstrate a good understanding of level of mathematics which although commensurate with the expected level of your course, they are taken from outside the syllabus.

An action towards this direction could involve using advanced statistical techniques or drawing on knowledge from other areas of mathematics.

For example, you could use multivariate analysis to analyze your data or even use concepts from calculus or probability theory.

5 Carry out personal experiments/questions of a sufficiently large sample size:

To demonstrate outstanding personal engagement, you should carry out personal experiments or questions of a sufficiently large sample size.

When conducting statistical analyses, it is important to ensure that your data is reliable and that your conclusions are valid. One way to ensure this is to use a sufficiently large sample size. A larger sample size reduces the impact of random variations and provides a more accurate representation of the population being studied.

6 Present your data in a visually appealing and informative way:

To demonstrate creativity and independence you can present your data in a visually appealing and informative way. This could involve creating graphs, charts, plots or other visual aids that help to convey your findings.

For example, you could create a scatter plot to show the correlation between two variables.

7 Explore the topic from different perspectives:

You can alwaysexplore the topic from different perspectives. One way to do so would be to attempt examining different factors that may affect the correlation between two variables or considering the limitations of your study.

For example, you could investigate how the correlation between athletic ability and academic performance varies across different age groups or consider the impact of confounding variables.

 8 Make and test predictions:

Personal engagement can often be shown through making and testing predictions.

You could involve using statistical models to predict future trends or outcomes based on your data.

For example, you could use linear regression to predict future academic performance based on athletic ability.

9. Clearly explain your thought process and methodology:

High level engagement is found when trying to clearly explain your thought process and methodology.

This action allows the reader/examiner to understand how you approach the exploration and the reasoning behind your methods.

For example, you could explain how you selected your sample size or why you chose a particular statistical test.

10. Consider the implications of your findings:

You can gain engagement credits by exploring the real-world applications of your results or considering how your study contributes to the field of statistics.

For example, you could investigate how your findings could be used to inform policy decisions or suggest avenues for future research.

Assume that you want to investigate the correlation between height and weight in a group of teenagers.

You can gain engagement credits by:

• Using a unique data collection method: Instead of simply surveying your classmates, you could gather data from a more diverse sample, such as students from other schools or countries through a broader questionnaire.

• Considering different factors that may affect the correlation found.You can investigate whether gender or age has an impact on the relationship between height and weight, or whether the results vary depending on the participants' level of physical activity.

• Making and testing predictions:Based on your collected data, you could create a linear regression model to predict a person's weight based on their height, and then you can proceed on testing the accuracy of your model.

• Clearly explaining your methodology and reasoning. You could explain how you selected your sample size and why you chose a particular statistical test to analyze the collected data.

• Considering and recognizing the implications of yout findings. You could investigate how their results could be used to from other professionals in future surveys.

Are there any things I should avoid doing ?

Avoid using textbook-style problems: Using problems straight out of a textbook without any personal input or creativity is unlikely to demonstrate personal engagement.

Avoid choosing a topic solely based on personal interest: While personal interest is important, it's not enough to demonstrate outstanding personal engagement. You need to show creativity, independence, and thought with which the mathematics is used or applied.

Avoid choosing a topic that is too narrow or too broad: Choosing a topic that is too narrow might limit your ability to show creativity and independence, while choosing a topic that is too broad might make it difficult to focus your investigation.

Avoid using a small sample size or inadequate statistical methods: Using a small sample size or inadequate statistical methods could limit the validity and reliability of your results.

Avoid ignoring ethical considerations: Ethical considerations should be taken into account when collecting data, and any potential harms to participants should be avoided.

Avoid neglecting the importance of clear and concise communication: Communicating your findings clearly and concisely is important to show that you have a complete understanding of the context of the exploration topic and the reader better understands the writer's intentions.