Sampling & simulation
Comfortably identify the purpose of sampling and simulation for making arguments about data, and employ techniques using software to differentiate a real-data result from random chance or “happenstance.”
K–2 Competencies
Describe characteristics of a population and recognize that variability exists within any population. e.g., jelly beans in a jar
Describe events as either likely, meaning they happen a lot, or unlikely, meaning they happen a little. e.g., pulling a purple jelly bean out of a jar with mostly red jelly beans
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
Recognize that a sample of a group may or may not reflect the entire group. e.g., if the class's favorite drink for lunch is chocolate milk, does that mean the school's favorite drink for lunch is chocolate milk?
Relate the effect of repeated samples to the representativeness of an entire group. e.g., pulling 10 jellybeans from a jar 5 times gives a better estimate of the color distribution than just one handful
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
Evaluate how different sampling methods impact the accurate representation of a population and their ability to generalize findings to other groups.
Assess how sample size impacts the accuracy of estimates representing population characteristics.
Identify the sources of potential bias in a sample or population, and describe how bias may impact the results of an investigation.
Describe what it means for an event to be likely or unlikely using probability. e.g., probability of 0 is unlikely, 1 is very likely, 1:2 is neither likely or unlikely
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
Use simulations in a digital software to help determine whether the results of an experiment are likely due to something other than random chance.
Analyze how dataset bias impacts sample results over time by introducing intentional bias sources in digital simulations and observing their effects.
Answer probabilistic questions resulting from a simulation.
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
Use simulation-based inferential methods at large N to draw conclusions from a dataset using digital software.
Identify why simulation can be used to infer conclusions about a population referencing the Law of Large Numbers.
Interpret margin of error and confidence intervals for a given sample.
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
Describe the relationship between the margin of error, confidence intervals, and standard deviation, in both words and in their formal mathematical definitions.
Execute and correctly interpret the margin of error, confidence interval, and standard deviation in a data analysis software for a given summary statistic.
Classroom resources
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