Google Data Analytics Professional Certification Practice Test

What does data integrity refer to?

1. The process of selecting a sample

2. The accuracy and completeness of data

3. The probability of obtaining meaningful results

4. The method of organizing data

The correct answer is: The accuracy and completeness of data

Data integrity specifically refers to the accuracy and completeness of data, which is crucial for ensuring that the information is reliable and meaningful for analysis. Maintaining data integrity involves various practices that protect data from corruption, unauthorized access, and other forms of alteration. This ensures that data remains true to its original form throughout its lifecycle, allowing analysts to draw valid conclusions from their analyses. The other options touch on different aspects of data handling. For instance, the process of selecting a sample pertains to sampling methods rather than the integrity of the data itself. Mention of the probability of obtaining meaningful results relates more to statistical significance and experimental design. Organizing data involves structuring and storing data effectively but does not inherently address the quality or correctness of the data. Thus, out of all these choices, the definition that aligns with the concept of data integrity is, indeed, the accuracy and completeness of data.