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IASSC ICBB Exam - Topic 2 Question 65 Discussion

Actual exam question for IASSC's ICBB exam
Question #: 65
Topic #: 2
[All ICBB Questions]

Which of these is not a primary cause for Non-normal Data?

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Suggested Answer: D

by Giovanna at Apr 28, 2024, 03:23 PM

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Nilsa
3 months ago
Granularity? Sounds more like a data quality issue.
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Tiffiny
4 months ago
Kurtosis is important, but not a primary cause.
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Noel
4 months ago
Wait, what's formulosis? Never heard of that!
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Lashon
4 months ago
I think mixed distributions are a big factor too.
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Mica
4 months ago
Skewness definitely affects normality.
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Genevieve
5 months ago
I’m leaning towards D as the answer since it doesn’t sound like a standard statistical term, but I could be wrong.
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Gilbert
5 months ago
I practiced a question similar to this, and I feel like skewness and kurtosis are definitely primary causes, but I can't recall what formulosis is.
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Glendora
5 months ago
I think mixed distributions definitely cause non-normality, but granularity sounds a bit vague to me.
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Geoffrey
5 months ago
I remember skewness and kurtosis being key factors for non-normal data, but I'm not sure about formulosis.
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Avery
5 months ago
Ah, this is a tricky one. I remember learning about the main causes of non-normal data, but those last two options are throwing me off. I'll have to rely on my understanding of the core concepts to eliminate the distractors and select the best answer.
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Mickie
5 months ago
Okay, let me see. Skewness and kurtosis are definitely primary causes of non-normal data, and mixed distributions can also lead to non-normal distributions. But I'm not sure what "formulosis" or "granularity" refer to in this context. I'll have to make an educated guess on this one.
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Brandon
5 months ago
Hmm, I'm a bit unsure about this one. I know non-normal data can be caused by things like skewness and kurtosis, but I'm not familiar with "formulosis" or "granularity" as potential causes. I'll have to think this through carefully.
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Maybelle
5 months ago
This looks like a straightforward question on the causes of non-normal data. I'll start by reviewing the key concepts around skewness, kurtosis, and mixed distributions.
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Izetta
5 months ago
Hmm, I'm a bit unsure about this one. I know macroeconomic data comes from government sources, but I'm not sure about the other options. I'll have to think this through carefully.
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Quentin
5 months ago
Okay, let's think this through step-by-step. First, I'll check the preview tab in the DataRaptor Load to make sure the Record ID is coming from the correct record type. Then I'll compare the Input JSON paths to the actual JSON structure to ensure they align.
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Shanda
5 months ago
This looks like a straightforward question about the features of Alibaba Cloud Anti-DDoS Premium. I'll carefully read through the options and select the 3 correct ones.
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Merilyn
5 months ago
Hmm, not sure about this one. I know automation is important, but I'm not totally clear on the different tools and techniques we can use. Might need to review my notes before answering.
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Lai
2 years ago
Cool, let's choose 'Formulosis' then. Thanks, everyone!
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Pura
2 years ago
True. I'd go with 'D) Formulosis' as an answer.
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Christiane
2 years ago
Agree. Skewness, mixed distributions, kurtosis, and granularity all sound valid.
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Daren
2 years ago
It does! I think 'Formulosis' isn't a real concept.
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Emmett
2 years ago
Yeah, doesn't 'Formulosis' look out of place?
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Lai
2 years ago
I'm a little confused by this question.
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