Question 8Fill in the blank: A data professional ensures their data analysis is fair by considering fairness from _____ to the point when their organization acts on the data insights. 1 pointthe data-collection phase of a projectthe insight-sharing phase of a projectthe start of a projectthe data-cleaning phase of a project
Question
Question 8Fill in the blank: A data professional ensures their data analysis is fair by considering fairness from _____ to the point when their organization acts on the data insights. 1 pointthe data-collection phase of a projectthe insight-sharing phase of a projectthe start of a projectthe data-cleaning phase of a project
Solution
The correct answer is: the data-collection phase of a project.
Step by step explanation:
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The question is asking at what point a data professional should start considering fairness in their data analysis.
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The options given are: the data-collection phase of a project, the insight-sharing phase of a project, the start of a project, and the data-cleaning phase of a project.
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Considering fairness from the start of a project is important, but it's not specific enough. The data-cleaning phase and the insight-sharing phase are too late to start considering fairness.
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Therefore, the best answer is the data-collection phase of a project. This is because fairness should be considered from the very beginning when the data is being collected. This ensures that the data is not biased and that the analysis will be fair.
Similar Questions
Question 6Data analysts ensure their analysis is fair for what reason?1 pointFairness helps them avoid biased conclusions.Fairness helps them pick and choose which data to include from a dataset. Fairness helps them communicate with stakeholders.Fairness helps them stay organized.
Question 3Which fairness best practice is intended to help data teams better understand the context surrounding their data analysis conclusions?1 pointUse oversamplingConsider relevant dataIdentify surrounding factorsInclude self-reported data
Question 1Which of the following statements accurately describe fairness considerations in data analysis? Select all that apply.1 pointBest practices for fairness in data analysis include identifying surrounding factors.Fairness practices should begin during the analyze phase of the data analysis process.Effective data analysts help create systems that are fair and inclusive to everyone. A data professional may include self-reported data when prioritizing fairness.
Question 6Which of the following are examples of fairness in data analysis? Select all that apply. 1 pointFactoring in social contexts that could create bias in conclusionsConsidering systematic factors that may influence dataMaking sure a sample population represents all groupsPicking and choosing which data to include from a dataset
Why should a data analyst only ask fair questions?1 pointFair questions do not offend people.Fair questions are biased.Unfair questions do not have answers.Unfair questions can provide data that is misleading.
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