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IIBA Exam CBDA Topic 1 Question 3 Discussion

Actual exam question for IIBA's CBDA exam
Question #: 3
Topic #: 1
[All CBDA Questions]

An analyst is working through data on comparing performance scores in different schools across the state, for ranking purposes. Since there is a lot of data and some extreme outliers, the analyst is trying to determine which type of statistical average would best represent the results. Which of the following is a concern when relying too heavily on summary statistics during data analysis?

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Suggested Answer: A

Summary statistics are numerical measures that describe certain characteristics of a data set, such as the mean, median, mode, standard deviation, range, or quartiles. Summary statistics can help simplify and communicate complex data, but they can also obscure or distort important information, such as the distribution, shape, outliers, or trends of the data. Contextualization is the process of providing relevant background information, assumptions, limitations, or explanations for the data analysis and its results. Contextualization can help avoid misinterpretation, confusion, or bias when using summary statistics. Contextualization can also help connect the data analysis to the business problem, objectives, and stakeholders.


by Bulah at Mar 05, 2024, 05:51 AM

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Charlena
5 months ago
I agree. It's important to take into account both contextual information and data variation when analyzing data.
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Dorsey
6 months ago
Definitely. Data variation can impact the choice of average and help account for outliers skewing the results.
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Shasta
6 months ago
But what about data variation? Isn't that also important to consider when choosing the right statistical average?
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Ulysses
6 months ago
That's true. The context in which the data was collected is crucial for proper interpretation.
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Charlena
7 months ago
I think relying too heavily on summary statistics can lead to overlooking important contextual information.
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