IIBA Exam CBDA Topic 2 Question 26 Discussion

Actual exam question for IIBA's CBDA exam

Question #: 26
Topic #: 2

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?

AContextualization

BData variation

CData properties

DFrequency

Show Suggested Answer

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 Jess at Oct 24, 2024, 05:23 AM

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Nidia

20 days ago

Haha, relying too heavily on summary stats? That's like driving with your eyes closed and expecting to reach your destination safely.

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Abel

1 days ago

C) Data properties

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Jenelle

2 days ago

B) Data variation

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Lina

4 days ago

A) Contextualization

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Gregg

29 days ago

Hmm, I was leaning towards C) Data properties. Understanding the properties of the data, like skewness or kurtosis, is crucial for selecting the right analysis approach.

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Gary

22 hours ago

C: That's a good point. D) Frequency is also important to consider, as it helps us understand how often certain values occur in the data.

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Tamekia

4 days ago

B: I agree, but I think A) Contextualization is crucial too. We need to consider the context in which the data was collected to make accurate interpretations.

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