2.7.1: Review Questions
- Page ID
- 58287
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Multiple Choice
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A customer satisfaction survey where respondents rate service as "Poor," "Fair," "Good," or "Excellent" produces data at which level of measurement?
- a) Nominal
- b) Ordinal
- c) Interval
- d) Ratio
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Which of the following is an example of semi-structured data?
- a) A relational database table of employee records
- b) A JSON file from a web API containing nested user profiles
- c) A handwritten letter scanned as an image
- d) A video recording of a meeting
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An organization estimates that 80–90% of its data is unstructured. This statistic primarily refers to:
- a) Data stored in SQL databases
- b) Data in spreadsheets and CSV files
- c) Text documents, emails, images, social media posts, and video
- d) Sensor readings from IoT devices
Short Answer
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Explain the difference between quantitative and qualitative data. Give two examples of each from a healthcare context, and describe which statistical operations are appropriate for each type.
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Compare internal and external data sources. Provide three examples of each for a retail company, and explain why combining both types yields richer analytics insights.
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Define three data quality dimensions (accuracy, completeness, consistency) and provide a real-world example where failure in each dimension led to negative consequences.
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What is metadata? Describe three types of metadata (descriptive, structural, administrative) and explain how each supports data quality and analytics workflows.
True/False
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True or False: Temperature measured in Celsius is ratio-level data because 0°C represents the complete absence of temperature.
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True or False: A dataset can be complete (no missing values) but still have poor accuracy if the recorded values are incorrect.
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True or False: Data governance is primarily a technical concern handled only by the IT department.
Answer Key:
- b
- b
- c
- Quantitative: blood pressure readings (continuous), number of hospital visits (discrete) — can compute mean, standard deviation. Qualitative: diagnosis categories (nominal), pain severity rating (ordinal) — can compute frequency counts, mode, but not meaningful averages.
- Internal: POS sales transactions, customer loyalty data, inventory records. External: census demographics, weather data, competitor pricing. Combining reveals context (e.g., correlating sales drops with weather patterns or competitor promotions that internal data alone can't explain).
- Accuracy: NASA Mars Orbiter lost due to unit mismatch (metric vs imperial). Completeness: missing ZIP codes in a mailing campaign lead to undeliverable mail. Consistency: customer name spelled differently across billing and support systems causes duplicate records and confusion.
- Metadata = "data about data." Descriptive: titles, keywords, summaries for discovering datasets. Structural: schema info, table/column names, relationships. Administrative: access permissions, creation date, retention policies. Each helps analysts find, understand, and trust data.
- False (Celsius is interval — 0°C is arbitrary, not true absence of heat)
- True
- False (data governance involves business stakeholders, policies, and culture — not just IT)