14. What results from starting an analysis with flawed data? (Choose 2 answers.)
Answer: A,C
Starting an analysis with flawed data leads to inefficient data management and skewed results.
Initiating an analysis with flawed data results in spending excessive time managing data rather than focusing on the analysis itself. Additionally, missing data can significantly distort the outcomes of the analysis.
A) More time is spent managing data than analyzing data.
This option is correct because flawed data often requires extensive cleaning and validation, diverting resources and time away from actual analysis. When data quality is poor, analysts must invest significant effort into correcting issues instead of deriving insights from the data.
B) Data must be put in a table or a chart so that errors can be more easily detected.
This option is incorrect as it does not directly address the consequences of starting with flawed data. While organizing data in tables or charts can aid in identifying errors, it does not resolve the fundamental issue of the data being flawed. It also implies an assumption that formatting will fix the underlying problems.
C) Missing data tend to skew the results of the analysis.
This option is correct since missing data is a common issue that can lead to biased or inaccurate results. When critical data points are absent, the analysis may draw conclusions that do not reflect the true situation, thereby misleading stakeholders.
D) Spreadsheets must be used to increase the likelihood of analyzing the flawed data.
This option is incorrect because using spreadsheets does not inherently increase the likelihood of effectively analyzing flawed data. In fact, reliance on spreadsheets without proper data governance can exacerbate issues related to data quality rather than resolve them.
Conclusion
The correct answers, A and C, highlight the significant impact of flawed data on the analysis process, emphasizing both the inefficiencies in data management and the potential for skewed results. Options B and D fail to address the core implications of flawed data and do not contribute to understanding the consequences of starting an analysis with such data.