# Data quality and cleaning

> An observation where a required measurement is absent.

Course: [Data science foundations](https://studybrix.com/en/courses/data-science/index.md)
Level: university
Prerequisites: Algebra and basic statistics.

## Notes

### Missing value

An observation where a required measurement is absent.

### Outlier

An observation unusually far from the main pattern and requiring contextual review.

### Data type

The representation and allowed operations for a variable.

### Validation rule

A stated condition used to check whether data are plausible or acceptable.

## Practice questions

### 1. Which term best matches this description: An observation where a required measurement is absent.

A. Missing value
B. Validation rule
C. Outlier
D. Data type

Answer: Missing value

Explanation: Missing value is the matching concept in this lesson.

### 2. Which term best matches this description: An observation unusually far from the main pattern and requiring contextual review.

A. Outlier
B. Data type
C. Missing value
D. Validation rule

Answer: Outlier

Explanation: Outlier is the matching concept in this lesson.

### 3. Which concept would you apply first in a situation where the representation and allowed operations for a variable.

A. Outlier
B. Data type
C. Validation rule
D. Missing value

Answer: Data type

Explanation: The scenario directly calls for Data type.

### 4. Name the concept described as: A stated condition used to check whether data are plausible or acceptable.

Answer: Validation rule

Explanation: The intended concept is Validation rule.

### 5. Which statement about Missing value is most accurate?

A. An observation unusually far from the main pattern and requiring contextual review.
B. A stated condition used to check whether data are plausible or acceptable.
C. The representation and allowed operations for a variable.
D. An observation where a required measurement is absent.

Answer: An observation where a required measurement is absent.

Explanation: The definition in the lesson notes gives the required condition.

### 6. Explain one important difference between Missing value and Outlier.

Sample answer / marking guidance: Missing value: An observation where a required measurement is absent. Outlier: An observation unusually far from the main pattern and requiring contextual review.

Explanation: Credit a clear distinction using both definitions and an appropriate example if supplied.

### 7. Give a short example showing how Data type would be used in practice.

Sample answer / marking guidance: A valid example should demonstrate this idea: The representation and allowed operations for a variable.

Explanation: Credit a relevant scenario that applies the concept rather than merely naming it.

### 8. Summarize why Validation rule matters in the lesson “Data quality and cleaning”.

Sample answer / marking guidance: Validation rule matters because A stated condition used to check whether data are plausible or acceptable.

Explanation: Credit the core purpose, scope and any important limitation from the notes.

## Further reading

- https://pandas.pydata.org/docs/
- https://www.openintro.org/book/os/

Original AI-assisted educational draft; not independently teacher-reviewed. References support further study, not copied exercises, affiliation or curriculum accreditation.