Module 2: Data responsibility

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In this post, I provide accurate answers and detailed explanations for Module 2: Data responsibility of Course 3: Prepare Data for Exploration – Google Data Analytics Professional Certificate.

Whether you’re preparing for quizzes or brushing up on your knowledge, these insights will help you master the concepts effectively. Let’s dive into the correct answers and detailed explanations for each question.

Here, we’ll walk through questions 1 to 15 with detailed explanations to support your learning.

To find answers to the remaining questions, check out the full module breakdown below:

Test your knowledge on unbiased and objective data

Practice Quiz

1. Which of the following are examples of sampling bias? Select all that apply.

  • A national election poll only interviews people with college degrees. ✅
  • A survey of high-school-age students does not include homeschooled students. ✅
  • An online marketing analytics firm stores data in a spreadsheet.
  • A clinical study includes three times more men than women. ✅

Explanation:
Sampling bias occurs when certain groups are overrepresented or underrepresented in a dataset, leading to unbalanced or non-representative samples. The examples listed show such biases:

2. Fill in the blank: The tendency to search for or interpret information in a way that validates pre-existing beliefs is _____ bias.

  • interpretation
  • confirmation ✅
  • sampling
  • observer

Explanation:
Confirmation bias occurs when individuals seek, interpret, or remember information in a way that aligns with their pre-existing beliefs or hypotheses, often ignoring evidence to the contrary.

3. Which of the following terms are also ways of describing observer bias? Select all that apply.

  • Research bias ✅
  • Perception bias
  • Spectator bias
  • Experimenter bias ✅

Explanation:
Observer bias occurs when a researcher or observer’s expectations influence their interpretation of data or behavior. This is also referred to as research bias or experimenter bias.

Test your knowledge on data credibility

Practice Quiz

4. Which of the following are usually good data sources? Select all that apply.

  • Academic papers ✅
  • Vetted public datasets ✅
  • Governmental agency data ✅
  • Social media sites

Explanation:
Academic papers, vetted public datasets, and data from governmental agencies are considered reliable and credible sources because they typically undergo rigorous validation processes.

5. To determine if a data source is cited, you should ask which of the following questions? Select all that apply.

  • Is the data relevant to the problem I’m trying to solve?
  • Has this dataset been properly cleaned?
  • Is this dataset from a credible organization? ✅
  • Who created this dataset? ✅

Explanation:
Evaluating whether a dataset is cited involves determining its credibility and origin. Questions about relevance or data cleaning, while important for data quality, are not specifically about whether a data source is cited.

6. A data analyst is analyzing sales data for the newest version of a product. They use third-party data about an older version of the product. For what reasons is this inappropriate for their analysis? Select all that apply.

  • The data is not current ✅
  • The data is biased
  • The data is not accurate
  • The data is not original ✅

Explanation:

  • The data is not current: Using older data can lead to incorrect conclusions since it doesn’t reflect the current state of the product or market.

  • The data is not original: Relying on third-party data might mean that the data was collected for a different purpose, potentially leading to mismatches or inaccuracies for the current analysis.

Test your knowledge on data ethics and privacy

Practice Quiz

7. Fill in the blank: _____ states that all data-processing activities and algorithms should be completely explainable and understood by the individual who provides their data.

  • Currency
  • Openness
  • Transaction transparency ✅
  • Privacy

Explanation: Transaction transparency ensures that individuals understand how their data is processed and used, promoting accountability and trust in data practices.

8. A data analyst removes personally identifying information from a dataset. What task are they performing?

  • Data anonymization ✅
  • Data visualization
  • Data collection
  • Data sorting

Explanation: Data anonymization is the process of removing personally identifiable information (PII) to protect individuals’ privacy while still allowing the data to be used for analysis.

9. Before completing a survey, an individual acknowledges reading information about how and why the data they provide will be used. What is this concept called?

  • Discretion
  • Consent ✅
  • Currency
  • Privacy

Explanation: Consent involves individuals agreeing to the collection and use of their data after being informed about the purpose and methods of data collection. It is a key principle of ethical data practices.

Test your knowledge on open data

Practice Quiz

10. What aspect of data ethics promotes the free access, usage, and sharing of data?

  • Privacy
  • Transaction transparency
  • Openness ✅
  • Consent

Explanation: Openness in data ethics encourages the unrestricted access, sharing, and usage of data to foster innovation and collaboration while maintaining ethical standards.

11. What are the main benefits of open data? Select all that apply.

  • Open data makes good data more widely available. ✅
  • Open data combines data from different fields of knowledge. ✅
  • Open data restricts data access to certain groups of people.
  • Open data increases the amount of data available for purchase.

Explanation: Open data promotes accessibility and integration, allowing researchers, organizations, and the public to utilize valuable data across various domains, leading to innovation and improved decision-making.

12. Universal participation is a standard of open data. What are the key aspects of universal participation? Select all that apply.

  • All corporations are allowed to sell open data.
  • Everyone must be able to use, re-use, and redistribute open data. ✅
  • Certain groups of people must share their private data.
  • No one can place restrictions on data to discriminate against a person or group. ✅

Explanation: Universal participation ensures open data is inclusive, non-discriminatory, and accessible to everyone, promoting equitable opportunities for innovation and knowledge-sharing.

*Module 2 challenge*

Graded Quiz

13. Fill in the blank: A preference in favor of or against a person, group of people, or thing is called _____. It is an error in data analytics that can systematically skew results in a certain direction.

  •  data collection
  • data interoperability
  • data bias ✅
  • data anonymization

Explanation:
Data bias systematically skews results, emphasizing the need for vigilance in data collection and analysis to ensure objectivity.

14. Which type of bias is the tendency to always construe ambiguous situations in a positive or negative way?

  • Observer
  • Confirmation
  • Sampling
  • Interpretation ✅

Explanation:
This happens when someone interprets vague data to match a positive or negative mindset, instead of being neutral.

15. Which of the following are qualities of unreliable data? Select all that apply.

  • Biased ✅
  • Inaccurate ✅
  • Vetted
  • Incomplete ✅

Explanation:
Good data should be fair, correct, and complete. Missing or skewed data leads to flawed analysis.

That’s it for Part 1! Continue your learning journey with the next set of answers.

Next Part: Module 2: Data Responsibility Answers (Part 2: Q16–30)

Part 2: Q16–30

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