Correlation versus causation
Comfortably separate correlation from causation in a wide variety of situations, building a “first-reaction” thinking habit over time.
K–2 Competencies
Classroom resources
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.”
3–5 Competencies
Using graphical displays, informally assess whether or not there is an association between two phenomenon in a data visualization and discuss whether one observation or trend may affect the other.
Classroom resources
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
Assess simple data to identify potential associations between two variables while considering that correlations do not imply causation and may arise from unobserved factors.
Using graphical displays, identify and categorize the type of potential relationship between pairs of numerical variables with terms such as independent, dependent, and covariate.
Classroom resources
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
Recognize that a randomized experiment is the best way to establish evidence for causation and justify a claim through isolating an effect of only one independent variable on another variable at a time.
Identify spurious correlations in the media and explore other potential causes that may explain these associations when applicable.
Classroom resources
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
Independently identify examples of two dependent variables that are both influenced by a third variable in real-world data. e.g., coffee consumption and lower risk of disease are both affected by an active lifestyle
Identify spurious correlations in the media and analyze how they relate to media claims and AI recommendations. e.g., ice cream sales and shark attacks both increase in the summer; they're both linked to hot weather, not each other
Classroom resources
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.”
Advanced Competencies
Explain why a chosen analysis method effectively isolates an effect.
Justify a causal relationship in a multivariable dataset with real-world data, including additional datasets gathered from outside sources and connect the analysis to existing research literature.
Classroom resources
Support other teachers by sharing a resource
Do you have a lesson plan, video, or tip that could help others teaching this topic?
Developed by our coalition
.png){kind=logo}
Coalition organizers
Share feedback on the Learning Progressions
Your feedback helps us improve these progressions for teachers around the world. Thank you!
Share feedback on the Learning Progressions
Your feedback helps us improve these progressions for teachers around the world. Thank you!
Share a classroom resource
Suggesting a resource helps students around the world learn essential data science skills.