Science and Engineering Practices

In the following sections, we first explain the synergy between each science and engineering practice and the five MDPs. We then provide multiple examples, options, and variations of activities and instructional strategies that are aligned with each MDP and the focal practice in order to be as comprehensive and specific as possible. However, this does not mean that teachers must use all of these strategies to enact the MDPs when promoting each science and engineering practice, nor that these strategies are the only way to do so. We encourage teachers to use their professional discretion to select what will work best for them and their classrooms, and to modify and innovate on these strategies, using the blank space provided at the end of each section for notes, reflections, and new ideas.

Analyzing and Interpreting Data

Practice 4: Analyzing and Interpreting Data

Belonging Supports for Analyzing and Interpreting Data

When analyzing and interpreting data in the classroom, students may work in teams, receive feedback from others, and collectively communicate results. Instructional strategies that support students’ feelings of belonging cultivate a safe space for students to hone skills for analyzing and interpreting data in these regards. Strategies that support belonging also encourage students to develop a sense of being part of a community of scientists and engineers, which is especially important for students who may not have a well-developed science identity or who may feel alienated from science [see Motivation as a Tool for Equity]. As students begin to feel a greater sense of belonging within their science classroom community and within science and engineering communities, they may feel more inclined to engage in the practices of analyzing and interpreting data.


Have students analyze and interpret data in groups to promote the idea of being a community of learners working to solve design problems or figure out phenomena. Working in small groups enables both scientific collaboration and innovation.
Structure whole class discussion so that discourse builds from simpler observations about patterns and trends to more complex ones, allowing students to build on one another’s ideas constructively and allowing validation of multiple ideas at different levels of complexity.
If there are multiple tasks that can be done simultaneously within groups, define roles and assign students (or have students select a role within their groups) so that everyone can contribute to the analysis and interpretation.
Allocate time for students to present findings and supporting evidence to peers and allow for student feedback/dialogue around data analysis and interpretation (e.g., what are the sources of error? How were significant features and patterns identified? To what extent does their data serve as evidence to support conclusions made?) As part of this conversation, students determine whether and describe why components of the practice (organization, visual displays, summarizing, patterns and relationships, sources of error, outlying data) are/are not appropriate to help them figure out a phenomenon or evaluate competing design solutions to a problem.
  • Set up norms for these conversations to establish a sense of belonging/comfort around how to analyze, interpret, and communicate results as evidence.
When feasible, give different groups different analysis tasks so that they can share later and make claims supported by evidence as a class or “lab team.” For example, use a jigsaw format so students engage in different analysis tasks in expert groups and share their evidence with their original groups.
Place an emphasis on the central tendency of data and data interpretation that allows everyone’s interpretation to merge into the “average” (consensus) of the classroom.

There is likely a wide variety of math ability levels in a single science class. Differences in skill may require different levels of scaffolding in order to develop confidence for all students. Some students may have little experience with data or may have limited confidence in successfully being able to tabulate, graph, or perform statistical analysis on data. Students may also be uncomfortable presenting the results of the analysis and interpretation to their peers. Supporting students’ confidence as they engage in these activities will be crucial for them to feel comfortable working with data.


Use prompts like “what do you notice?”, “what do you observe?”, or “what questions do you have?” to give students an accessible entry point into data analysis and an early experience of success in working with data to figure out phenomena or solve design problems.
Explicitly name the skills and strategies needed to interpret data and graphs and provide opportunities to practice these skills, so that students view data interpretation as something they can learn to master with practice.
Provide self-questioning stems or thinking guides that help students to systematically interpret and analyze different kinds of data independently. Thinking guides can also help students evaluate the analysis and interpretation of data.
Give students practice identifying trends and interpreting a common set of data rooted in understanding a phenomenon or solving a problem so that they can receive informational feedback before they do the same tasks with their own data.
Cater analysis to students’ current abilities – e.g., if students struggle with graphing data, give them examples (sample created by the teacher for the particular task, examples of past student work) or options for how to represent their data (bar, line, or pie chart).
Provide scaffolds such as thinking guides and checklists for common data analysis tasks that prompt students to consider which type of data analysis is most appropriate to help them figure out a phenomenon or solve a design problem. For example:
  • Guidelines for graphing: a checklist for the parts of a graph, thinking guides for students to determine a good scale for the data they are graphing or the type of graph that will be most useful for their purpose
  • Guidelines for data tabulation: checklists for how to set up a frequency table, how to set up a table for the different variables in an experiment, etc.
  • Guidelines for summarizing data: different summary statistics (e.g., mean, median, mode) and what information they provide scientists and engineers

Data collection, especially by young scientists and engineers with limited experience, can contain a large amount of error. During analysis, having a learning orientation will help frame that error as a critical part of the learning experience (both learning techniques for how to reduce the error in future data collection and learning how to account for error in data interpretation) rather than a failure. Supporting a learning orientation will also help with encouraging students to engage deeply with their data to make sense of a phenomenon or design solutions to design problems, rather than merely performing calculations or getting the “right” answer.


Ask students to identify what patterns they see in a data set and develop their own hypotheses about what that pattern can tell them about evidence for other phenomena (e.g, the forces exerted by a rocket can inform you about the forces of other engine-propelled devices).
Engage students in error analysis to figure out what went wrong during data collection, where errors were made (including if there was uncertainty in measurement) and how these errors are reflected in the data, and why different students or different lab groups may have obtained discrepant results. Emphasize that making errors is a normal part of learning how to make precise and accurate measurements and that, even when you have developed those skills, there will always be error in your measurements (e.g., estimating to the nearest tenth of a millimeter on a ruler with millimeter hash marks).
Model and then scaffold how to judge the appropriateness and correctness of data analysis and interpretation. For example, design data analysis questions or assessments such that they include an opportunity for students to explain the thinking behind their analyses/interpretations. Provide feedback and/or evaluate these responses based on the skills students demonstrate and their reasoning and evidence, rather than just the percent of correct/incorrect answers.
When the teacher makes a mistake in a computation, model a response that frames the mistake as normal and a learning opportunity, rather than becoming defensive about the error.
Use think-alouds to model a learning orientation to students; they can be used to normalize struggle, confusion, and mistakes and to model effective strategies for analyzing and interpreting data.
When discussing the interpretation of data, use the Learning Orientation Talk Moves or resources like the Accountable Talk Sourcebook, the “Supporting Discussions” chapter of the Open SciEd Teacher Handbook, Talk Science Primer, and Discourse Primer for Science Teachers to elicit multiple student perspectives before concluding which interpretations are supported by the data and to encourage students to build on each other’s ideas, critique each other respectfully, and acknowledge each other’s contributions. These discourse and facilitation moves help to demonstrate that the goal of the discussion – and data analysis in general – is to think deeply about the data and not just to arrive at the right answer.

Cognitive autonomy is especially important to encourage students to make their own decisions about how to analyze or make sense of the data, as well as to generate alternative interpretations and explanations. Working with data might make teachers prone to undermine student autonomy if they suggest that there is a clear “right” answer, such as a predetermined set of similarities and differences between two data tables that the teacher is leading students to identify. There might also tend to be an overemphasis on smaller autonomy allowances (e.g., letting students choose the colors for a graph) without accompanying demands on students’ cognitive autonomy in making sense of phenomena or problem solving. It is important to provide sufficient time and scaffolding for students to engage in rigorous, autonomous sense-making through the analysis and interpretation of data.


A way of scaffolding data analysis and interpretation early in the year would be to present students with several ways to summarize data and several types of graphs and have students choose which type they think will best summarize and present the data they have collected. Prompt students to make observations about the different affordances and limitations of each option and explain why they made their choice (e.g., ask students to justify their selection of tools and procedures through questions like, Why are you using a line graph for this data? What will using a map of the data tell you that a table might not? Why did you choose that kind of graph? Why did you average? Why did you select the way you did?).
Once students have developed more advanced data analysis/interpretation skills and are familiar with different ways of presenting data, allow students greater choice in selecting how to present their data (e.g., tables, graphs, flowcharts, illustrations) and prompt them to justify their choices.
Before holding a whole-class discussion about data, divide students into groups with open-ended prompts to help them make sense of the data and identify initial patterns and relationships within the data (e.g., similarities and differences, temporal and spatial, linear and nonlinear). These patterns and relationships can help students to figure out a phenomenon or identify the best characteristics among several design solutions that can inform a new, optimal solution. One way that students can engage in these sense-making activities is through graphing. The small group structure places responsibility on students to engage in the work, and positions the teacher as a facilitator, circulating among groups rather than directing a whole-class conversation on data interpretation.

Some students may have lower confidence in their ability to tabulate, graph, or perform statistical analyses on data or may even think they are not a “math person.” Framing data analysis within a phenomenon or design problem that is of interest to students may help motivate them to work hard on the mathematics needed for data analysis. Connecting the practice of analyzing and interpreting data to the work of scientists and engineers may help encourage students to see the value of math as a tool to make sense of the world. Encouraging students to connect data analysis and interpretation to a broad range of situations that relate to their lives and home communities can make them more invested in the practice as something that can be leveraged to figure out phenomena or solve problems that feel relevant and important to them [see Motivation as a Tool for Equity].


Talk with students about the goals of the data analysis (e.g., what questions are being asked of the data to make sense of a phenomenon or solve a design problem?) to make clear that analysis steps need to be relevant to the group-specified goal.
Find and regularly incorporate data representations that are relevant to students’ interests and/or daily lives (e.g., analysis of food nutritional content that shows a variety of snack foods that students enjoy).
Engage students in discussions and explorations that emphasize that data analysis and interpretation is an authentic science and engineering practice.
Share (or invite students to share) personal or historical stories of when data analysis and interpretation led to great advancements (e.g., Rosalind Franklin, Watson, and Crick and DNA double helix; Katherine Johnson’s calculations for NASA; Grace Hopper’s computer coding protocols).
Choose situations that students are familiar with and/or refer to the work of diverse scientists and engineers for data analysis and interpretation activities (e.g., population density as a variable related to microbiology that can affect the exponential spread of an infectious disease, such as during a pandemic).
Connect data visualization examples to the work that scientists and engineers do (e.g., “These are some cool ways that scientists communicate their findings”) through varied forms of data representation (e.g., bar graphs, Venn diagrams, models, flow charts, maps) in lessons across units.
Use real world datasets from organizations like NOAA, NASA, or others that share data and visualizations publicly.
Many strategies from equitable teaching frameworks (e.g., culturally responsive pedagogy) address ways to learn more about the local community and their needs, and to connect science and engineering learning to those needs.