| A | B | C | D | E | F | G | H | I | J | |
|---|---|---|---|---|---|---|---|---|---|---|
1 | Building Blocks for Data Literacy: What can my students do with data? | Last updated 4/27/21 | ||||||||
2 | This work is in development by K. Hunter-Thomson & M. Schauffler (CC 2021; v3 to be released by June 2021) | Creative Commons Attribution-ShareAlike 4.0 International License | ||||||||
3 | ** Read down a column to see the range of skills within a grade band AND/OR across a row to see the development of a skill over time (aka what to build off of and towards) ** | |||||||||
4 | ||||||||||
5 | Realm | Skill Area | Tasks | Grades K-2 Skills | Grades 3-5 Skills | Grade 6 Skills | Grades 7-8 Skills | Grade 9 Skills | Grades 10-12 Skills | Supporting references |
6 | Get Data | Ask Questions & Consider Possible Outcomes | Connect data, questions, and predictions | - Understand that data are observations, counts, or measurements of something. - Describe expectations based on prior knowledge (e.g., what do you expect to see, what do you think will happen?). | - Consider what question, problem, phenomenon, or need led to collection of a set of data. - Understand data as numbers or observations in a context. - Anticipate what data might show about a context or question. | - Understand that the questions that can be asked depend on what you have for data. - Pose 'meta' questions throughout an investigation. (e.g., What is the problem or context of the dataset? Which attributes are needed for the question? How does the prediction relate to the data?) | - Pursue questions that can be investigated with available resources. - Frame questions clearly, in a way that informs how to graph & analyze the data. - Make a predictive statement that could be tested with a simple experiment. | - Anticipate what can and cannot be inferred from the data you are gathering. - Evaluate a question to decide if it is testable and relevant to the phenomenon or problem. | - From a question, frame a null hypothesis that can be feasibly tested. | English, 2017 Ben-Zvi & Aridor-Berger, 2012 NGSS: SEP1, SEP3, SEP4, SEP6, SEP7 |
7 | Formulate questions in a given context | - Express curiosity; observe things and wonder about them. - Phrase wonderings in the form of questions. - With the help of a teacher, discuss what kind of information is needed to answer questions. - Recognize different kinds of questions (e.g. yes-no, why, how, comparisons, opinion, numeric, descriptive). | - Describe features of phenomena that are measurable. - Reflect on the context of a dataset and pose initial questions - interpret and critique initial questions. - Anticipate data collection and refine questions, with prompting. - Start asking 'meta' questions about data (e.g., Do we have enough data?, Which attributes should I include in the graph?) | - Reflect on the context of a dataset before forming a question. - Distinguish between factual questions (those that anticipate a single correct answer) and statistical questions (those that anticipate variability in data). - Pose 'statistical' questions that a given dataset can address. - Critique and modify questions to focus an investigation. | - Formalize something that is unknown into a question that can be feasibly investigated. - Ask questions about association between two numeric factors. - Reframe a question as an informal hypothesis. | - Ask questions to draw inferences or to generalize about populations or phenomena. - Reframe questions as a hypothesis that can be tested. | - Simplify a problem into logical steps; reframe, represent, or describe problems using abstractions. - Ask questions to clarify and/or refine a model or theory. | Franklin et al, 2005 (GAISE Report) Mayes, Peterson & Bonilla, 2013 English, 2017 English, Watson, & Fitzallen, 2017 CCSS-M: 6.SP NGSS: SEP1, SEP3 | ||
8 | Generate new questions | - With teacher assistance, raise new questions when looking at outcomes or thinking about the original question. | - Identify new questions after reviewing results and answering the original question. | - Identify new questions that stem from unexpected results and/or to clarify results. | - Pose additional questions about a set of data to more deeply explore a phenomenon or problem. - Raise questions about provided arguments and/or interpretations of data. | - Identify sub-questions that a set of data can address in order to help answer an overarching question. | - Pose new questions raised by data or findings for further investigation to better understand phenomenon or problem. | NGSS: SEP1, SEP3 | ||
9 | Generate Data | Design investigations | (Preparatory) - Discuss different ways to observe or measure a phenomenon to help answer a question. | (Preparatory) - Identify what features to measure for a given question or purpose - Consider the value of measuring more than one event or sample. - Realize that you can't always measure everything, so you measure some (a "sample"). - Evaluate results and revise investigation methods if needed. | - Design ways to systematically collect observations and conduct simple experiments, working with peers. - Understand that samples are a subgroup of a whole group. - When a whole group can't be measured, use strategies to select samples from the group that are representative. | - Distinguish between investigations that are surveys, observational studies, and experiments. - Design surveys, observational investigations and simple experiments with peers or individually. - Recognize the importance of random samples and sample size to reduce bias. | - Design a plan for data collection that suits an observational or experimental investigation. - Frame a hypothesis that could be tested experimentally. - Consider possible confounding factors, and account for them in designing an investigation. | - Design and conduct a randomized controlled experiment or sample survey to generate data to compare two treatments, or to make inferences about a population. - Design a safe and ethical investigation. | Ben-Zvi et al., 2012 Cobb & Moore, 1997 Watson & Moritz, 2000 GAISE Report, 2020 CCSS-M: 7.SP, S-IC NGSS: SEP3, SEP4 | |
10 | Collect data | - With teacher guidance or as a group, physically record observations or simple measurements (in a shared table or using manipulatives). | - Explain how and when data were collected. - Connect data collection with a question, problem, or context. | - Collect observational data (e.g., systematically record observations & measurements). - Collect data from simple experiments (e.g., with a control & repeated trials). - Consider how to collect data as accurately as possible. - Access data that someone else collected. | - Consider how much data will be needed in a given context. - Consider how to collect data that are as close as possible to the actual value (accuracy) and (when making repeated measurements) are as close as possible to each other (precision). - Collect data that reasonably represent a larger population. - Use sensors or simulations to generate data. | - Generate data using digital tools. - Create and/or revise a numeric model or simulation to generate data for question or problem. - Report data at level of accuracy appropriate to limitations of measurements. | - Use multiple lines of evidence when investigating a question or problem. - Evaluate consistency of measurements and observations across different data sources. - Refine models or simulations to reduce limitations of results. | Wild & Pfannkuch, 1999 CCSS-M: N-Q.3 NGSS: SEP2, SEP3, SEP4, SEP5 | ||
11 | Quantify Data | Identify cases & values | n/a | - Understand that each time something is observed and recorded, it produces a case. - Each point in a graph represents the value for a case. | - Understand that each graphed point represents one or more attributes of a single case. - Identify cases and case values using real-world data with units | - Recognize a case as a discrete event when one or more attributes were recorded. - Recognize cases as rows in a table. | Apply previous skills in combined or more complex ways. | Apply previous skills in combined or more complex ways. | ||
12 | Grasp the attributes | - Describe different attributes of objects (or groups of objects) such as color, size, or shape. - Decide whether an attribute is described by a word (e.g. color, shape) or by a number (e.g. how many), | - Identify categories, quantities, and ranges within an attribute. - Recognize categorical attributes as those with discrete groupings (categories), - Recognize quantitative attributes as those with numeric measures that have a range along a continuous scale. | - Understand attributes in the context of the dataset (e.g. what was measured or observed, how measurements were made, and possible reasons why they were measured). - Expect attribute values to vary. | - Consider how two attributes might relate to each other in the context of the dataset (e.g. one influences another, but not the other way around.) - Recognize a potential cause-and-effect relationship between stimulus (independent) and response (dependent) attributes. | - Critique the attributes in terms of an investigation and/or broader context or problem (e.g., Are attributes sufficient? Relevant? Likely reliable?). | Apply previous skills in combined or more complex ways. | Mayes et al., 2014 CCSS-M: K.MD, 1.MD, 2.MD, 6.EE NGSS: SEP5 | ||
13 | Use units | - Make measurements in relative units (arm lengths, steps, etc.) - Include those units when recording and describing observations. | - When making measurements, specify units used. - Convert units from one magnitude to another (e.g., cm to m). - Identify the units for an attribute by looking at the headers in a data table or by reading axis labels. | - Estimate approximate quantities in given units. - Convert measurement units to a different scale (e.g., °F to °C, miles to km). - Recognize how units of different magnitudes or scales are related (e.g. 1 km = 1,000 m). - Understand that two units expressed as ratios are rates (e.g. mph, price per pound). | -Recognize why ratios between measures with the same units are 'unitless'. | - Understand how units follow through in formulas and/or equations. | Apply previous skills in combined or more complex ways. | Mayes, Peterson & Bonilla, 2013 Bennett & Briggs, 2008 CCSS-M: 1.MD, 2.MD, 4.MD, 5.MD, N-Q | ||
14 | Organize & Process Data | Arrange data in tables | - Record observations or measurements in a pre-made table or list. | - Record and organize observations in a pre-made table. | - Design simple hand-drawn tables. - Enter data into hand-drawn tables or spreadsheets (with attention to accuracy and organization). | - Organize data in hand-drawn tables with multiple columns. - Design digital spreadsheets organized by cases (in rows) and attributes (columns). - Reorganize components of a spreadsheet to facilitate data collection or analysis. | - Modify spreadsheets or reorganize data as needed to maximize opportunities for data analysis. | Apply previous skills in combined or more complex ways. | Konold, Finzer, & Kreetong, 2017 CCSS-M: 7.SP, N-VM | |
15 | Interact with tables dynamically | - Given a table, count how many items are in a group and/or within a category. | - Find information in a table to answer a question. - Review the attributes (column headers) in a data table to identify what was measured, and how measurements were made. | - Identify the terms and concepts pertaining to a spreadsheet (i.e., cell, column, row, values, labels). - Filter information from a table. | - Solve multi-step real world problems using a sequence of steps (e.g., filter or extract necessary data from a large dataset to investigate a specific question or problem). | - Dynamically interact with tables to move data from collection to visualization and analysis using digital tools (e.g. simulations, sensors, and data libraries). | Apply previous skills in combined or more complex ways. | Lee & Wilkerson, 2018 CCSS Progression 3-5 Draft CCSS-M: 1.MD NGSS: SEP4, SEP5 | ||
16 | Realm | Skill Area | Tasks | Grades K-2 Skills | Grades 3-5 Skills | Grade 6 Skills | Grades 7-8 Skills | Grade 9 Skills | Grades 10-12 Skills | Supporting references |
17 | Explore Data | Visualize Data | Read graphs & maps | - Read simple representations of data to retrieve information (e.g., pictographs, dot plots (also called 'line plots'), bar charts, or maps. | - Grasp the structure of data represented in dot plots, bar charts, pie charts, line graphs and maps. - Read information from these graph types. - Identify the measurements that are represented in a graph, map, or legend, and point to features in the graph or map that represent them. - Describe the scale of values along an axis and identify units. | - Grasp the structure of and read information from box plots, histograms, and a two-dimentional scatter plot with x and y scales. - Recognize that a graph scale does not have to start at zero; the axis scale accommodates the range of the data. For example, an x-y plot often does not necessarily include all four quadrants. | - Read information from and evaluate all previously mentioned graph types. - Recognize that the colors in a graph or map represent attribute values or categories, not necessarily actual colors. | - Apply prior knowledge of graphs to make sense of new or specialized graph types. - Recognize that different graph types can communicate different kinds of information about the same data. | Apply previous skills in combined or more complex ways. | Cooper & Shore, 2010 CCSS-M: 1.MD, 2.MD, 3.MD, 6.NS, NGSS: SEP4, SEP5 |
18 | Create graphs & maps | - Create pictographs, bar charts, and dot plots ('line plots') to visualize counts of things and to group objects by their features. - Create graphs using manipulatives or draw and color them by hand. | - Explore data visually to search for interesting patterns. - Create pie charts to show proportions of categories. - Create bar charts to compare counts or averages. - Scale a single numeric axis (i.e., a dot plot or bar graph) with appropriate scale intervals. - Plot points with positive values in XY space to show how a point can have an X and a Y value at the same time. - Create graphs and scale axes by hand or using interactive technology. | - Decide what kind of graph is appropriate for a stated question. - Create frequency plots (dot plots, box plots, histograms) to show the distribution of points within a group. - Scale an axis (or axes) appropriately for a given set of data (i.e., suitable data range & intervals). - Critique and revise graphs to improve them. - Create graphs by hand or using interactive technology. | - Put suitable attributes on the axes for a given question. - Create scatter plots (to show association), stretched bar charts (to show proportional amounts), and hierarchical tree diagrams (to show tiered or relational data). - Decide when it is appropriate to connect XY points to make a line graph (i.e., to show continuous vs. discrete change). - Revise or enhance graphs based on peer discussion and feedback. | - Create combined or other graph types that show more complex patterns or relationships (e.g., bubble charts, area graphs). - Choose an appropriate graph type to display evidence for a specific conclusion. - Create graphs using digital technology. - Revise and enhance graphs to communicate a clear and compelling story to an audience. | - Create informal, hand-drawn, back-of-the-envelope graph sketches to explore conjectures and ideas before engaging in formal data generation or analysis . - Create graphs that communicate statistical support for a specific argument or inference. | Cobb & Moore, 1997 Cooper & Shore, 2010 CCSS-M: 2.MD, 3.MD, 4.MD, 5.MD, 5.G, 6.SP, 8.EE, 8.SP, A-CED, N-Q, S-ID NGSS: SEP4, | ||
19 | Filter, simplify, or transform data to reveal patterns | Calculate statistical values | - Calculate total counts in a group or in a category as a way to represent the size of the group. | - Subtract the minimum from the maximum to find the range of a group of numbers. - Calculate mean and median of a group of numbers as a way to represent where the 'center' of a group is. | - Use mean and median as ways to approximate what is typical in a group. - Recognize mode as representing a 'central cluster', or two 'central clusters' in a bimodal dataset. | - Recognize that statistical values provide only partial information about a dataset. - Use a linear equation to determine rate of change (slope) and scalar relationship (i.e., the value of y when x=0; the y-intercept). | - Calculate standard deviation and use it to quantify variation in a distribution. - Compare the rates of change of two factors by comparing the equations of the best fit lines. | - Interpret the nature of association using a least squares line and correlation coefficient, or, if appropriate plot and analyze residuals. | CCSS-M: 1.MD, 6.SP, 8.EE, 8.SP, A-REI, S-ID NGSS: SEP4 | |
20 | Compute or transform attributes | n/a | - Convert units from one order of magnitude to another (e.g., cm to m). | - Convert measurement units to a different scale (e.g., °F to °C, miles to km) - Filter a set of data to only show relevant categories and/or ranges. | - Combine values from two attributes to derive a new attribute using simple computations (e.g. totals, ratios, or meaningful groupings) - Represent numerical data as categorical groups to help reveal patterns. - Calculate relative frequencies across rows or columns in a two-way table to reveal patterns of association. | - Compute new attributes from existing ones in dataset using more complex logic or models (e.g. nonlinear functions, conditional logic, or other Boolean functions). | - Test and refine use of transformed attributes, extend their use to new situations, and/or explain and justify their use. | Mayes et al., 2014 CCSS-M: 8.SP | ||
21 | Describe & Analyze Patterns | Recognize & describe variability | n/a | - Describe properties of a group such as how many are in the group ('n'), or the range of values. - Recognize that values in a group usually vary. - Notice that values in some groups vary more than they do in other groups. - Informally consider what is typical in a group. | - Develop intuitive ideas about variability (e.g., variability is everywhere; some groups vary a little, some vary a lot). - Recognize variability along one axis as how spread out points are from a center. - Describe variability as a property of a group (distribution) in terms of range, center, and how the data are clustered (shape). | - Include variability when comparing two or more groups (distributions). - Distinguish between variability within a group and variability between groups. - Recognize variability in XY space as how dispersed points are from a trend line. - Make informal conjectures about possible explanations for patterns in variability. | - Connect patterns in variability to the context of the dataset. - Use and evaluate models to quantify variability (e.g., confidence intervals, linear & nonlinear functions, residuals). - Understand the difference between natural variability and variability introduced by sampling or measurement methods. | - Use investigative strategies to handle and account for variability in data collection, analysis, and interpretation. - Apply quantitative tools to determine statistical significance. - Use variability to predict & evaluate random samples or outcomes. - Understand the statistical term 'error' to mean the difference between a measured value and its actual value. | Dierdorp et al, 2017 Fife, James, & Peters, 2020 Franklin et al, 2007 (GAISE Report) Garfiled & Ben-Zvi, 2005 Makar, 2013 CCSS-M: 6.SP, 7.SP, S-IC | |
22 | Describe visual patterns | - Sort objects according to similarities or differences. - Describe similarities and differences between groups of objects. - Articulate relative values or quantities among groups using words such as most, least, more than, etc. | - Describe an attribute in terms of its categories or range(s) of data. - Understand that values on a numeric scale are continuous/consecutive. - Compare relative values in a table or graph (e.g., is more than, less than). - Informally describe how two groups of objects are similar and how they differ. - Describe patterns in graph that are relevant to a question. | - Describe and reason about properties of a distribution of data (e.g. range, clustering patterns.) - Describe what appears to be typical for a group (central clumps, averages, or interquartile range). - Acknowledge values in a distribution that are far outside the range of others. | - Compare similarities and differences between two distributions (e.g., ranges, relative position, centers, distribution shape). - Identify extreme values (or outliers) as different from what is typical for the group, and part of natural variability (unless there is a clear reason not to do so). - Visually distinguish between linear & non-linear relationships in XY scatter plots. - Describe the nature of linear associations in XY scatter plots informally at first (e.g. positive or negative, and strong, weak, or non-existing), then quantitatively using a linear equation. | - Review a set of data for consistency and quality. - Re-evaluate a working explanation in light of new data. - Describe how attributes change in relation to each other (e.g., increase/decrease, abrupt/gradual/cyclical, stable/changing). - Describe quantitative features of patterns, such as intervals of change, relative maximums and minimums, symmetries, or periodicity. | - Recognize patterns of signal within noisy data (such as patterns within subsets of a data range) as clues to understanding a problem or phenomenon. | Konold & Pollatsek, 2002 Cobb, 2005 CCSS-M: K.CC, K.MD, 1.MD, 2.MD, 3.MD, 6.EE, 6.SP, 7.SP, 7.SP, 8.F, 8.SP, F-IF NGSS: SEP3, SEP4, SEP5 | ||
23 | Do things to reveal or highlight patterns | n/a | - Visually highlight or label important features or patterns in bar charts, pie charts, line graphs, or pictograms. | - Visually highlight or label important features of distributions (box plots, histograms, dot plots). | - Add a best fit trend line and describe it using slope and y-intercept. - Zoom in to isolate patterns in parts of a graph. - Sort or filter data to clarify patterns. - Highlight quantitative features or patterns to emphasize or clarify meaning. - Recognize that changing the scale of a quantitative axis can change the apparent pattern of the points. | - Enhance graphs with quantitative or statistical features to highlight evidence. | Apply previous skills in combined or more complex ways. | Erickson et al., 2019 | ||
24 | Model (quantify) patterns | n/a | n/a | - Use distribution shape to help decide which measure of center best summarizes what is 'typical' for a group: mean, median, interquartile range, or mode for bimodal data. - Identify potential limitations of a simulation or numeric model. | - Show a linear xy relationship informally (sketched by hand or digitally). - Describe the strength and nature of the relationship informally at first, (based on direction of slope, and how close points are to the line), then quantitatively (using the slope and y-intercept). | - Describe or represent linear patterns mathematically using equations or computations. - When an association is nonlinear, identify which kind of nonlinear function best describes the relationship. | - Mathematically model nonlinear relationships. - Use statistical measures (i.e., Chi-squared, t-test, regression equations, confidence intervals, standard error) to formally verify the significance of patterns and relationships. | CCSS-M: 6.SP, 8.F, 8.SP, A-CED, S-ID, F-BF, F-LE NGSS: SEP 4 | ||
25 | Realm | Skill Area | Tasks | Grades K-2 Skills | Grades 3-5 Skills | Grade 6 Skills | Grades 7-8 Skills | Grade 9 Skills | Grades 10-12 Skills | Supporting references |
26 | Infer Meaning from Data | Interpret Data to Learn Something | Interpret patterns in context (e.g., engage with questions, conjectures, claims, predictions, & models to think statistically) | - Informally discuss which information is useful (and which is not useful) for answering a given question. - Refer to patterns in a graph to answer a given question. - Frame observations about patterns as declarative statements, e.g. "More of the pets are dogs than cats." | - Make informal conjectures about what a pattern in a graph could mean in terms of a stated question. | - Describe features or patterns in graphs and maps that say something about a stated question or prediction. - Interpret what features of a distribution say about the nature of a group. - Begin thinking about change as continuous (i.e., represented in a line graph), rather than as discrete, singular events. - Make a conjecture or write a claim that is based on patterns in data. | - Decide whether groups are the same or different based on comparison of their distributions. - Articulate what slope and y-intercept of a linear model mean in the context of the data. - Recognize correlation without assuming causation. - Draw conclusions or make predictive statements that are based on one or more patterns in data. | - Think statistically about conclusions. - Analyze data from more than one persepective to enhance interpretation. - Decide whether a relationship is likely causal or not. - Consider physical mechanisms that could explain one attribute as independent (stimulus) and another dependent (responding). - Interpret qualitative and quantitative features of graphs and maps in the context of the inquiry. | - Interpret models of data in the context of questions, conjectures, claims, or predictions. - Understand what nonlinear relationships mean in context. - Relate multiple features of data or lines of evidence when putting together or evaluating a provided claim from data. | Watson & Moritz, 2000 Biggs & Collis, 1982 de Beer, Gravemeijer, & van Eijck, 2015 CCSS-M: 2.MD, 3.MD, 4.MD, 5.MD, 5.G, 6.NS, 6.SP, 7.SP, 8.SP, F-LE, S-ID, S-IC NGSS: SEP4, SEP7 |
27 | Justify an interpretation | - Recognize whether or not a statement is supported by the data at hand. - Justify why some features of a graph or data were selected and not others. - Listen to provided arguments to determine agreement or disagreement based on the evidence in data. | - Identify features of a graph or table of data that support (or refute) a prediction or provided claim. | - Think about whether or not a pattern is meaningful in a given context. - Explain how aspects of a graph support or refute a prediction or claim. - When justifying a claim, take variability in the data into account. | - Discuss how the data support (or refute) a prediction or claim. - When justifying a claim, refer to quantitative aspects of the data. | - Explain the extent to which the data support or refute a formal hypothesis. | - Use formal statistical reasoning in the context of the dataset to justify whether the data support or refute a null hypothesis. - Take possible confounding effects into account when reasoning about a claim. - Make and evaluate assumptions when estimating, modeling, and interpreting data. | Ben-Zvi et al, 2012 Mayes et al, 2013, 2014 Watson & Moritz, 2000 CCSS-M: 6.SP, 7.SP, F-IF, S-ID NGSS: SEP1, SEP2, SEP3, SEP4, SEP5, SEP6, SEP7 CCC2 | ||
28 | Infer broader meaning | - Informally consider what a graph says (or does not say) in a bigger context (e.g., Do you think this means that dogs are always more popular than cats in every first grade class?) | - Working with peers, make informal inferences that extend beyond the given dataset. ( e.g. Would your conclusions apply to a larger group?) - Informally reason about the validity of a specific inference. | - Distinguish between making a claim about a particular dataset and making an inference about a wider context. - Turn a claim into an inference about a broader population or phenomenon. - Discuss whether or not an inference is reasonable. | - Draw inferences about a whole population from a sample. - Discuss the limits of what can be inferred from a sample. - Evaluate whether or not a specific inference is reasonable. | - Compare and critique arguments (inferences) in terms of the evidence used to support them. | - Evaluate inferences in terms of factors such as validity, reliability and (or) potential confounding factors. | Makar & Rubin, 2018 Fielding-Wells & Makar, 2015 Ben-Zvi & Aridor, 2012 AAC&C, 2010 CCSS-M: 7.SP, N-Q, S-IC, S-IC, N-Q NGSS: SEP2, SEP3, SEP7 | ||
29 | Evaluate Uncertainty | Refer to uncertainty when reasoning about data | n/a | - Recognize that there is usually some uncertainty involved when making a claim. - Develop language to convey shades between being 100% certain and 100% uncertain when making claims. | - Reason informally about aspects of a pattern that contribute to certainty or uncertainty in a claim. - Relate that reasoning to confidence in a claim. - Describe aspects of a model or simulation that may make results uncertain (e.g., potential limitations of a model or simulation.) | - Informally consider degree of certainty when making a claim. - Estimate the probability that a data point or pattern occurred by chance. - Informally evaluate how the degree of imprecision or inaccuracy affects certainty in conclusions. - Phrase inferences to reflect degree of uncertainty. | - Consider the extent to which study design, sample selection, or methods affect quality of data and certainty of claims or inferences. - Reason about the extent to which accuracy and/or precision can affect certainty. - Phrase claims or inferences using probablistic language (e.g., the data suggest that..., results indicate...). | - Use simple statistical measures to quantify level of confidence when identifying differences between groups (i.e., Chi-squared, t-test) and to quantify fitness of a linear relationship (R^2) - Make and test statistical hypotheses by calculating p-value and/or confidence intervals. | Madden, 2021 Makar & Rubin, 2018 Ben-Zvi et al., 2012 Konold et al., 2014 CCSS-M: 6.SP, 7.SP, 8.SP, N-Q NGSS: SEP4, SEP6, SEP7, SEP8 | |
30 | Evaluate limitations of data | n/a | - Discuss how imprecise tools or careless measurement can affect how sure you can be when making claims from data. - Revise claims as needed. | - Reflect on factors that can affect how well a sample represents the whole population or phenomenon (e.g., sampling bias or methods of measuring). - Consider factors that can affect certainty in claims or inferences (e.g. natural variability, small sample size, sampling bias, measurement error, imprecise tools). - Revise claims or inferences to strengthen them. | - Understand that inaccuracy and/or imprecision of measurements can weaken a claim or inference. - Recognize that a claim about a given set of data may be very certain, but a wider inference based on the same data may be uncertain (for example, the sample of data might not represent the wider phenomenon or population very well). | - Include limitations of data when developing arguments (inferences) about wider population or phenomenon. - Critique competing arguments (inferences) in terms of new evidence or limitations of the data. | Apply previous skills in combined or more complex ways. | CCSS-M: N-Q, S-IC NGSS: SEP2, SEP3, SEP4, SEP7, SEP8 | ||
31 | Use or Build on New Knowledge | Decide what to do with new information | n/a | - Recognize differences between wishful thinking, opinion, speculation, evidence-based statements, and facts. - Use "give-and-take conversation" to exchange ideas about observations, tables, graphs, and interpretations. | - Recognize a purpose behind analyzing and interpreting data (e.g., to address a question, gain insight into a phenomenon, test a claim, or justify an action with evidence). - Share and critique graphs with peers and revise to clarify evidence. | - Discuss questions, graphs, patterns and reasoning with peers as a means to develop and improve arguments. - Develop a habit of giving and getting critique about evidence through discourse among peers. | - Engage with peers in scientific discourse that is grounded in data and evidence. - Make and justify decisions based on evidence-based arguments. - Identify next steps to further clarify or analyze observed patterns. | Apply previous skills in combined or more complex ways. | Michaels, 2015 Michaels & O’Connor, 2015 Cobb & McClain, 2004 Konold & Higgins, 2003 Wild & Pfannkuch, 1999 NGSS: SEP7 | |
32 | Follow through with an action (e.g., communicate findings, justify decisions, design new investigation) | - Describe a story or idea that emerged from recorded observations or measurements. - Connect ideas or decisions to observations or measurements. | - Communicate findings verbally and in writing, supporting ideas with data tables, graphs, and maps. | - Communicate findings in informal oral or written presentations to peers. - Consider implications of results in a broader context (self, community, or broader understanding). - Apply what was learned from an investigation to propose a solution or next step. | - Justify an argument, action, or next step with quantitative and qualitative information and reasoning. | - Communicate findings using spoken, written, visual, digital, and analog formats. - Relate findings to relevant problems or phenomena and propose potential solutions or next steps. - Recognize that decisions that are made based on evidence may need to change as new data emerge. | - Use language and technology effectively to “tell a story” that emerges from data to different audiences. - Weigh decisions based on evidence, taking into account uncertainty and probabilities of risks and benefits the context of a situation. | Michaels, 2015 NGSS: SEP8 | ||