Concept D3.6
Media Literacy & Digital Citizenship

Subset effects

Recognize that important information may be hidden or may even change a major conclusion when data is filtered into categories and/or groups.

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Data Science Starter Kit Module 4: Drawing Conclusions - Interpreting Problems and Results

Welcome to one of the most critical skills in data science—learning how to draw valid conclusions from data and understanding what those conclusions can and cannot tell us. This module focuses on the thinking skills that separate good data science from misleading claims.🔗

Interpreting Problems and Results isn’t about complex statistical tests or advanced mathematics. It’s about developing the intellectual honesty and critical thinking skills to say, “Based on this data, here’s what we can reasonably conclude, here’s what we’re not sure about, and here’s what we still need to investigate.”

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Data Science Starter Kit Module 4: Drawing Conclusions - Interpreting Problems and Results

Welcome to one of the most critical skills in data science—learning how to draw valid conclusions from data and understanding what those conclusions can and cannot tell us. This module focuses on the thinking skills that separate good data science from misleading claims.🔗

Interpreting Problems and Results isn’t about complex statistical tests or advanced mathematics. It’s about developing the intellectual honesty and critical thinking skills to say, “Based on this data, here’s what we can reasonably conclude, here’s what we’re not sure about, and here’s what we still need to investigate.”

6–8 Competencies

Summarize variables in a dataset with measures of central tendency with both the full data and with subsets (e.g., occupation, race, gender, income, zipcode, education).

6-8.D.3.6a

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Data Science Starter Kit Module 4: Drawing Conclusions - Interpreting Problems and Results

Welcome to one of the most critical skills in data science—learning how to draw valid conclusions from data and understanding what those conclusions can and cannot tell us. This module focuses on the thinking skills that separate good data science from misleading claims.🔗

Interpreting Problems and Results isn’t about complex statistical tests or advanced mathematics. It’s about developing the intellectual honesty and critical thinking skills to say, “Based on this data, here’s what we can reasonably conclude, here’s what we’re not sure about, and here’s what we still need to investigate.”

9–10 Competencies

Create and compare subsets of a dataset with software.

9-10.D.3.6a

Discuss examples of aggregate measures of data that missed important subsets in the media or other real-world contexts.

9-10.D.3.6b

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Data Science Starter Kit Module 4: Drawing Conclusions - Interpreting Problems and Results

Welcome to one of the most critical skills in data science—learning how to draw valid conclusions from data and understanding what those conclusions can and cannot tell us. This module focuses on the thinking skills that separate good data science from misleading claims.🔗

Interpreting Problems and Results isn’t about complex statistical tests or advanced mathematics. It’s about developing the intellectual honesty and critical thinking skills to say, “Based on this data, here’s what we can reasonably conclude, here’s what we’re not sure about, and here’s what we still need to investigate.”

11–12 Competencies

Identify and explain Simpson’s Paradox: an average trend may disappear or even reverse when individual subsets and/or groupings are examined.

11-12.D.3.6a

Review examples of Simpson’s Paradox in the media and in well-known research studies.

11-12.D.3.6b

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Data Science Starter Kit Module 4: Drawing Conclusions - Interpreting Problems and Results

Welcome to one of the most critical skills in data science—learning how to draw valid conclusions from data and understanding what those conclusions can and cannot tell us. This module focuses on the thinking skills that separate good data science from misleading claims.🔗

Interpreting Problems and Results isn’t about complex statistical tests or advanced mathematics. It’s about developing the intellectual honesty and critical thinking skills to say, “Based on this data, here’s what we can reasonably conclude, here’s what we’re not sure about, and here’s what we still need to investigate.”

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