Probablistic language
When communicating with others, employ both plain-language and clear vocabulary to regularly describe degrees of uncertainty, both formally and informally as a thinking habit.
K–2 Competencies
Recognize that some situations are not binary and find appropriate vocabulary to describe them. e.g., a classmate riding the bus to school could be "always," "sometimes," or "never"
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
Formulate a guess or hypothesis and identify informal vocabulary to convey your level of confidence. e.g., I strongly believe most of my classmates ride the bus to school because XX or YY
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
Express a finding and quantify your confidence in it by stating the degree of certainty regarding the result. e.g., I am highly confident that a majority of students in my area ride the bus to school, based on separate sources' estimations of 60%, 62.3%, and 65% of students ride the bus to school
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
Clearly state a result or finding and indicate the level of certainty regarding a formal statistical concept alongside an informal evaluation of the likelihood of the event.
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
Clearly state the result or finding and indicate the level of certainty regarding the statistical analysis and the quality of the evidence (e.g., dataset or source characteristics, similar findings in alternative data) as justification.
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
Clearly state a result or finding, along with the degree of certainty, using two or more advanced statistical methods (e.g., probability distributions, t-tests, z-tests, or bootstrapping/simulation), while justifying the conclusions with evidence (e.g., dataset or source characteristics, similar findings in alternative data) quality indicators like dataset characteristics, source reliability, and corroborating findings from alternative data.
Classroom resources
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