Chapter 1: Introduction to Data-Driven Ethics Quiz
Test your understanding of data-driven ethics, the scientific method, and foundational concepts for ethical analysis.
1. What distinguishes data-driven ethics from traditional moral philosophy?
- It replaces moral reasoning with algorithms
- It combines quantitative analysis with ethical reasoning to address systemic harm at scale
- It focuses exclusively on individual moral dilemmas
- It ignores philosophical arguments entirely
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The correct answer is B. Data-driven ethics combines quantitative analysis with ethical reasoning to address systemic harm at scale. It doesn't replace moral judgment with algorithms, but rather gives our moral judgment better information to work with by measuring harm and identifying effective interventions.
Concept Tested: Data-Driven Ethics
2. Why does the course focus on analyzing 16 specific industries rather than individual ethical dilemmas?
- Individual dilemmas are too complex to analyze
- Industries are easier to regulate than individuals
- A small number of industries account for the vast majority of preventable human suffering
- Philosophers have already solved all individual ethical dilemmas
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The correct answer is C. The course focuses on industries because concentrated harm demands concentrated attention. A small number of industries (tobacco, fossil fuels, ultra-processed foods, etc.) cause the vast majority of preventable human suffering and environmental destruction, making them high-priority targets for intervention.
Concept Tested: Evidence-Based Ethics
3. Which step of the scientific method involves proposing explanations and interventions?
- Observe
- Hypothesize
- Analyze
- Conclude
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The correct answer is B. The hypothesize step involves proposing explanations for observed patterns of harm and potential interventions to address them. This comes after observation and before testing.
Concept Tested: Scientific Method
4. What is the primary danger of confirmation bias in ethical analysis?
- It makes research more expensive
- It causes researchers to search for and interpret evidence in ways that support pre-existing beliefs
- It reduces the number of available data sources
- It makes conclusions too objective
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The correct answer is B. Confirmation bias is the tendency to search for, interpret, and remember information that confirms what we already believe. In ethical analysis, this can lead to dismissing contradictory evidence and producing biased conclusions.
Concept Tested: Confirmation Bias
See: Bias Recognition
5. What is source triangulation?
- Using three sources that all share the same methodology
- Analyzing data from only triangular geographic regions
- Using multiple independent sources to verify claims
- Prioritizing sources that triangulate with your hypothesis
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The correct answer is C. Source triangulation means using multiple independent sources to verify claims. If three different sources using different methods reach similar conclusions, you can be more confident in those conclusions than if you relied on a single source.
Concept Tested: Source Triangulation
6. Which type of analysis helps answer the question "Why do people behave the way they do?"
- Quantitative analysis
- Statistical analysis
- Qualitative analysis
- Numerical analysis
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The correct answer is C. Qualitative analysis deals with non-numerical information like stories, experiences, motivations, and meanings. It helps answer questions about why people behave as they do, how affected communities experience harm, and what barriers prevent change.
Concept Tested: Qualitative Analysis
See: Qualitative Analysis
7. According to the chapter, which of the following is NOT a factor that affects data credibility?
- Methodology used to collect the data
- Whether findings have been replicated by independent sources
- The political affiliation of the researchers
- Whether the source has a conflict of interest
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The correct answer is C. Data credibility depends on methodology, transparency, replication, recency, and conflict of interest. While bias can affect research, political affiliation alone is not a standard factor in evaluating data credibility. The key is whether proper methods were followed regardless of who conducted the research.
Concept Tested: Data Credibility
8. What is the relationship between objectivity and subjectivity in data-driven ethics?
- Data-driven ethics is purely objective with no subjective elements
- The measurement of harm can be relatively objective, but what counts as harm involves subjective value judgments
- All ethical claims are purely subjective and cannot be analyzed with data
- Objectivity and subjectivity are the same thing in ethical analysis
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The correct answer is B. Data-driven ethics occupies interesting territory between objectivity and subjectivity. The measurement of harm (deaths per year, healthcare costs) can be relatively objective, but what counts as harm and how much weight to give different harms involves value judgments that are ultimately subjective.
Concept Tested: Objectivity and Subjectivity
9. Which of the following is a PRIMARY source of data?
- A peer-reviewed journal article summarizing previous research
- A government statistical report from the Census Bureau
- A survey you design and conduct for your specific analysis
- An NGO report analyzing existing data
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The correct answer is C. Primary sources are original data collected directly for your analysis, such as surveys you design and conduct, experiments you run, observations you make, or interviews you conduct. Government reports, journal articles, and NGO reports based on others' data are secondary sources.
Concept Tested: Primary Sources
10. According to the data-driven ethics process, what is the ultimate goal of analyzing harmful industries?
- Publishing academic papers about industry practices
- Identifying leverage points where small changes can yield large impacts
- Collecting as much data as possible about each industry
- Assigning blame to corporate executives
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The correct answer is B. The six-step data-driven ethics process (harm identification, quantification, causal analysis, systems mapping, intervention analysis, and leverage point identification) ultimately aims to identify leverage points where small, well-placed changes can yield large impacts on reducing harm.
Concept Tested: Data-Driven Ethics
See: Putting It All Together: The Data-Driven Ethics Process