Question 1Which of the following statements accurately describe fairness considerations in data analysis? Select all that apply.1 pointFairness practices should begin during the prepare phase of the data analysis process.Fairness means ensuring that analysis does not create or reinforce bias. Best practices for fairness in data analysis include considering all available data.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

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 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 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 6A data professional at a grocery store considers fairness when collecting data. Rather than having store associates share observations, they create a survey that asks customers to provide information about their own shopping experiences. This helps avoid any unconscious bias that might be introduced by the sales associates. Which fairness best practice does this scenario describe?1 pointOversamplingUsing all available dataConsidering contextSelf-reporting