Difficulty: Easy

Average Score: 100%

What results from starting an analysis with flawed data? (Choose 2 answers.)

Rationale

More time is spent managing data than analyzing data and missing data tend to skew the results of the analysis.

Starting an analysis with flawed data leads to inefficiencies, as significant time is often diverted to correcting or managing the data rather than focusing on the actual analysis. Additionally, incomplete or incorrect data can introduce biases, skewing the results and undermining the reliability of conclusions drawn from the analysis.

A (More time is spent managing data than analyzing data.) CORRECT

When flawed data is present, analysts must invest considerable time in cleaning, validating, and correcting the data before any meaningful analysis can occur. This can result in a substantial delay in the analytical process, diverting focus away from deriving insights.

B (Data must be put in a table or a chart so that errors can be more easily detected.) YOUR ANSWER

While organizing data in tables or charts can help visualize errors, it does not inherently resolve the issues caused by flawed data. This choice suggests a solution rather than a consequence of starting with flawed data, making it less relevant to the question.

C (Missing data tend to skew the results of the analysis.) CORRECT

When data is missing, it can lead to incomplete analyses and biased results. Missing data can create gaps that distort the overall picture, leading analysts to draw incorrect conclusions based on the available information, thus affecting the integrity of the analysis.

D (Spreadsheets must be used to increase the likelihood of analyzing the flawed data.) YOUR ANSWER

Although spreadsheets can be useful for data analysis, they do not specifically address the problem of flawed data. The need to use spreadsheets suggests a method rather than a consequence of flawed data and does not directly relate to the impact of starting with such data.

Conclusion

Beginning an analysis with flawed data results in increased time spent on data management and can significantly skew analysis outcomes due to missing information. These consequences highlight the importance of data integrity in analytical processes, as flawed data not only complicates the workflow but also jeopardizes the validity of the results.

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