Search Results
Data & AI
Recognize that data “fuels” AI, that AI can be compared to a function machine (math), algorithm (CS), or a prediction model (statistics) that relies on data to both operate and improve itself, and that AI tools can also be used to analyze complex data in research.
Data & AI
Recognize that data “fuels” AI, that AI can be compared to a function machine (math), algorithm (CS), or a prediction model (statistics) that relies on data to both operate and improve itself, and that AI tools can also be used to analyze complex data in research.
Apply context
Recognize that the context surrounding the data and the investigation shapes interpretation. Many fields (biology vs. psychology; economics vs. sociology) have created very different frameworks to organize problems. Considering multiple approaches may reveal useful insights from the same data.
Apply context
Recognize that the context surrounding the data and the investigation shapes interpretation. Many fields (biology vs. psychology; economics vs. sociology) have created very different frameworks to organize problems. Considering multiple approaches may reveal useful insights from the same data.
Frequency tables
Organize data into frequency tables based on shared characteristics. Summarize data using counts, fractions, relative frequencies, or proportions to enable comparisons and generalizations. Understand the implications of choices made when creating and interpreting frequency tables.
Frequency tables
Organize data into frequency tables based on shared characteristics. Summarize data using counts, fractions, relative frequencies, or proportions to enable comparisons and generalizations. Understand the implications of choices made when creating and interpreting frequency tables.
Comparing variability
Examine differences between groups by analyzing measures of spread, such as range and standard deviation. Utilize visualizations like box plots and apply statistical methods, including mean, median, and standard deviation, to compare datasets, assess variability, and uncover patterns in data distributions and models.
Comparing variability
Examine differences between groups by analyzing measures of spread, such as range and standard deviation. Utilize visualizations like box plots and apply statistical methods, including mean, median, and standard deviation, to compare datasets, assess variability, and uncover patterns in data distributions and models.
Explaning significance
Clearly describe the basic logic of statistical significance to others, differentiating between significance, the size of an effect, and the statistical power of an analysis. Recognize what statistical significance can reveal and cannot reveal about a phenomenon.
Explaning significance
Clearly describe the basic logic of statistical significance to others, differentiating between significance, the size of an effect, and the statistical power of an analysis. Recognize what statistical significance can reveal and cannot reveal about a phenomenon.
Sense-making with visualizations
Practice creating visualizations to summarize many things at once, relationships between things in one place, or exceedingly complex ideas in one place. Recognize that visuals can be more efficient or compelling than other forms of communication.
Sense-making with visualizations
Practice creating visualizations to summarize many things at once, relationships between things in one place, or exceedingly complex ideas in one place. Recognize that visuals can be more efficient or compelling than other forms of communication.
Apply the logic of Bayes Theorem to determine whether a data-based claim in the media was accurately explained.
11-12
50
Justify the Expected Value equation (EV = P (Xi) * Xi) with formal probability statements and by explaining the Law of Large numbers.
11-12
50
Apply the Expected Value equation to assess its fitness for the problem by determining the accuracy of the estimate based on the number of trials conducted. e.g., flipping a coin 100 times and determining if getting heads 30 times is reasonable when the expected value is getting heads 50 times
11-12
50
Explain the concept of statistical significance (e.g., including its role in distinguishing meaningful results from random chance) in plain language and the limitations of significance testing (e.g., inability to address study design flaws, confounding variables, or real-world validity beyond a narrow model comparison).
11-12
50
Describe how statistical significance tests are constructed, calculated, and interpreted in the context of chosen probability models and/or assumptions.
11-12
50
Identify real-world instances where assessing statistical significance is crucial (e.g., scientific studies to distinguish actual effects from random variation) while also evaluating the significance claims made by others and recognizing situations where statistical significance is necessary but not sufficient for proving a point.
11-12
50
11-12.D.1.4d Differentiate statistical significance, effect size, and statistical power in simple terms with real-world examples, explaining how each addresses distinct questions in research. e.g., whether outcomes could be connected to random chance, the meaningfulness of impacts in context, the suitability of the analysis approach to the specific data and problems
11-12
50
Use simulation-based inferential methods at large N to draw conclusions from a dataset using digital software.
11-12
50
Identify why simulation can be used to infer conclusions about a population referencing the Law of Large Numbers.
11-12
50
Interpret margin of error and confidence intervals for a given sample.
11-12
50
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
11-12
50
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
11-12
50
Recognize that randomization can happen in various settings, regardless of the intervention or events involved. e.g., artificial interventions, accidental or chance events, unrelated to the question of interest
11-12
50
Differentiate between lab experiments and natural experiments in scenario-based questions.
11-12
50
Use computer software to analyze the relationship between an independent and dependent variable in a linear model by changing the number and combination of dependent variables.
11-12
50
Evaluate how changes to the number and combination of dependent variables affect the model by interpreting R-squared and regression coefficients.
11-12
50
Explore how polynomials of different degrees fit scatterplots.
11-12
50
Analyze how increasing or decreasing the degree of a polynomial can lead to potential overfitting or underfitting the data.
11-12
50
Develop a causal diagram to map relationships among multiple variables and create an iterative analysis plan to test each relationship with data.
11-12
50
For query-based questions, estimate a confidence interval and margin of error in a real-world data analysis project with software.
11-12
50
For hypothesis-based questions, estimate a p-value based on a proposed statistical model for real-world data with software.
11-12
50
Highlight unusual associations or outcomes in an analysis document by validating analysis steps and investigating other parts of the dataset.
11-12
50
Identify potential counter-arguments or alternative explanations that may refute one's conclusions drawn from data, and suggest mitigation strategies that could be tried in the future with additional data or new research.
11-12
50
Evaluate the potential limitations of statistical findings by considering the data collection methods, sample selection, and simplifications that may not capture the complexity of real-world scenarios.
11-12
50
Determine if a causal claim can be established based on the investigation's design (e.g., natural experiments, real-world observations) and describe the differences between expectations and the design.
11-12
50
Analyze a data generalization issue in media or real-world situations and discuss its significant impacts and the importance of addressing generalization errors.
11-12
50
Implement multiple strategies to generalize data-based conclusions to new populations or situations. e.g., add additional context or control variables, repeat the analysis with new collection or sample, test a model with a different dataset
11-12
50
Evaluate the advantages and disadvantages of automated tools that rely on large datasets for universal predictions. e.g., prediction algorithm for airline ticket prices or home mortgage application assessment, AI model for facial recognition, autonomous vehicle model trained on city roads
11-12
50
Evaluate the suitability of different sampling methods (e.g., random sample with or without replacement) for the specific question and available data.
11-12
50
Identify situations in which data on the full population is easily available or even critical to answer a question of interest, and traditional sampling-methods are not required.
11-12
50
Make an informal power analysis for an analysis or experimental setup using real-world data and a hypothesis, including claims about the 1) Effect Size 2) Sample Size 3) Statistical Significance and 4) Statistical Power.
11-12
50
Use the simple equation Power = 1 - β to visually show the difference between a normal distribution of outcomes and an abnormal distribution of outcomes.
11-12
50
Propose and implement at least two methods to mitigate sample bias in a real-world dataset. e.g., adding additional data, making a new variable with a correction, explicitly stated assumption
11-12
50
Identify and implement at least two strategies in a project-based activity that utilize original data to address questions in a new scenario.
11-12
50
Describe potential ethical and statistical issues with the extension strategies, including explicit caveats on any conclusions reached with real-world data.
11-12
50
Identify and explain Simpson’s Paradox: an average trend may disappear or even reverse when individual subsets and/or groupings are examined.
11-12
50
Review examples of Simpson’s Paradox in the media and in well-known research studies.
11-12
50
Recognize the importance of many trials, study validation, and meta-analyses in academic research.
11-12
50
Document data analysis steps in a shareable and reproducible format for collaboration platforms.
11-12
50
Create data visualizations that illustrate complex bivariate relationships. e.g., exponential, quadratic
11-12
50
Edit data visualizations to optimize it for your intended audience and the audience's different needs. e.g., "chartjunk" can be distracting for some audience but necessary for others
11-12
50
Create data visualizations of raw data and increasingly aggregated forms of the same data to help understand the nuances of the data.
11-12
50
Strategically use data visualization to identify potential outliers, errors, and unexpected findings, while clearly stating and justifying any reasons for excluding certain potentially erroneous observations.
11-12
50
Provide context for the data to help viewers understand the background and implications.
11-12
50
Recognize how color theory (e.g., tint, saturation, shading) can be used to represent continuously scaled data (e.g., darker color =higher concentration of occurrence).
11-12
50
Recognize that we have culturally-influenced or domain-specific ways of using and interpreting chart elements. Consider the conventions that are known to or expected by your audience when developing data visualizations.
11-12
50
Understand how uncertainty around point and effect estimates are communicated on data visualizations with error bars.
11-12
50
Evaluate the effectiveness of data visualizations, including the risk of misleading the reader.
11-12
50
Compare and/or contrast various representations of relative frequencies and proportions, identify elements of each representation that facilitate or hinder the identification of relative proportions, and explain the reasoning behind conventions. e.g., ordered or unordered stacked bar graph
11-12
50
Compare and/or contrast various ways to represent distributions and their measures of center (e.g., histograms, density plots, box plots) by plotting two distributions on the same graph and explaining how different representations facilitate or hinder the visibility of differences and associations.
11-12
50
Produce a data visualization parallel to the type of data (e.g., numeric, categorical, string, image, unstructured).
11-12
50
Defend your visualization choice to others and explain the data type and visualization type including suitability for continuous or discrete variables.
11-12
50
Evaluate the degree to which visualizations and their surrounding text match and support a real-world argument or broader explanation of social, economic, scientific, or political factors.
11-12
50
Make and defend arguments using key features from a data visualization.
11-12
50
Clearly define the claim by making it specific, measurable, and actionable.
11-12
50
Ensure the data directly addresses the claim being defended.
11-12
50
Address potential confounding variables and factors in claim-making, and if possible, demonstrate how the data controls for those confounding variables and factors.
11-12
50
Discuss a claim's broader implications in writing, including societal effects. e.g., a graph showing declining crime might ignore rising cybercrime
11-12
50
Write data analyses and stories using plain-language vocabulary along with relevant problem-specific terms, ensuring adaptability to various audiences, both technical and non-technical, with clear explanations of why the content is important for each audience.
11-12
50
Provide multiple representations of data relevant to individual arguments. e.g., visualizations, summary statistics, and descriptions of processes or methodologies
11-12
50
Communicate and present the source of the data used for the data visualization to ensure transparency.
11-12
50
Examine the significance of the data being visualized by understanding what it measures and its relevance to real-world issues or scenarios.
11-12
50
Examine how institutions (e.g., government, businesses, nonprofit organizations) utilize big data to achieve policy goals while considering the benefits and harms to the public and their implications for civic behavior.
11-12
50
Explain how data science connects to other disciplines to solve major problems around the globe.
11-12
50
Discuss strategies to mitigate harmful predictions derived from a data story, such as the varying injury rates from crash test dummies among different groups of drivers.
11-12
50
Develop democratic dispositions through evaluation of local data. e.g., review local election data, housing data in local city or county
11-12
50
Pick a local issue of student interest and based on a data analysis project, submit a Public Comment.
11-12
50
Consider the environmental and human costs of harvesting natural resources for the creation of modern technologies. e.g., mining of lithium, geopolitical issues with high precision silicon
11-12
50
Distinguish when data is categorical versus numeric and define the difference.
3-5
20
Recognize that non-traditional forms (e.g., photographs, written text, audio recordings) of data are informative and supportive of inquiry.
3-5
20
Understand case structure as a way to identify the defining "case" of the data where a case is a data point which may have many variables associated with it, each with a possible value.
3-5
20
Ask questions about how data are collected or considered.
3-5
20
Understand that data is generated by people who make decisions about what and how to measure.
3-5
20
Multiple conclusions can be drawn from the same set of data.
3-5
20
Recognize that variability of data contributes to uncertainty. e.g., measuring plant growth daily shows natural variation which makes predicting exact height for the next day difficult
3-5
20
Select variables of interest for data investigations while recognizing those selections will retain inherent limits.
3-5
20
Recognize AI as a computing tool that adapts its functions by acquiring knowledge from organized data inputs and outputs. e.g., AI tools improve their tasks by comparing outputs to correct answers such as a photo-sorting app checks if its ‘cat’ labels match human-provided tags, then updates its sorting rules to reduce mistakes
3-5
20
Recognize that many inputs and outputs can be organized into a structure that is easily readable by a machine (e.g., data-table).
3-5
20
Identify how data collection can create risks (e.g., medical information, location, privacy, exclusion) for individuals or groups, and describe ways to protect personal information.
3-5
20
Evaluate how datasets can benefit society (e.g., solving problems, improving designs) while considering potential risks to individuals.
3-5
20
Recognize that some biases in data are neutral, while others can be harmful when making decisions and inferences, and some may not cause harm at all. e.g., neutral, preference of apples over oranges in a fruit study; harmful, surveying the coding club to generalize about all students
3-5
20
Understand the importance of considering the context, scope, and purpose of data in order to mitigate bias.
3-5
20
Compare arguments with and without data.
3-5
20
Plan and conduct investigations to answer questions using basic data organization and visualization.
3-5
20
Revise questions and methods at each stage of investigation based on new findings.
3-5
20
Explain how inferences shift as new data emerges during an investigation.
3-5
20
Recognize that data interpretation varies across social and cultural contexts.
3-5
20
Describe the ways in which data can affect your personal life and habits.
3-5
20
Look through data to identify missing data, and add additional cases or values for variables if needed.
3-5
20
Look through data to identify unreasonable values or recording errors in data values, and correct these if the correct values are known.
3-5
20
Collect and organize data about objects or events with multiple variables, progressing from simple case cards to structured tables with labeled rows (e.g., observations) and columns (e.g., variables).
3-5
20
Manipulate tabular data by grouping cases based on categorical variables (e.g., grouping roller coaster cases so that all wood coasters are together and all steel coasters are together) and ordering cases based on numerical variables (e.g., ordering roller coaster cases "top speed" from slowest to fastest).
3-5
20
Compare characteristics across groups using basic numerical summaries (e.g., comparing the typical recess activity across different grade levels).
3-5
20
Create basic summaries that describe what is the same or different about groups in a dataset (e.g., summarizing how children of different ages differ in their favorite sports).
3-5
20
Design an investigation requiring collection of data involving the collection or gathering of multiple variables.
3-5
20
Design an investigation that require collecting numerical data, including looking at a variable over a period of time.
3-5
20
Record outcomes of simple random simulations or processes.
3-5
20
Use data generated by sensors or automated techniques. e.g., weather stations record temperature every hour
3-5
20
Describe the procedures and tools to be used to measure a quantity of an object or an event.
3-5
20
