SEP4: Analyzing and Interpreting Data

from NGSS Appendix F: Science and Engineering Practices in the NGSS

Once collected, data must be presented in a form that can reveal any patterns and relationships and that allows results to be communicated to others. Because raw data as such have little meaning, a major practice of scientists is to organize and interpret data through tabulating, graphing, or statistical analysis. Such analysis can bring out the meaning of data—and their relevance—so that they may be used as evidence. 

Engineers, too, make decisions based on evidence that a given design will work; they rarely rely on trial and error. Engineers often analyze a design by creating a model or prototype and collecting extensive data on how it performs, including under extreme conditions. Analysis of this kind of data not only informs design decisions and enables the prediction or assessment of performance but also helps define or clarify problems, determine economic feasibility, evaluate alternatives, and investigate failures. (NRC Framework, 2012, p. 61-62)  

As students mature, they are expected to expand their capabilities to use a range of tools for tabulation, graphical representation, visualization, and statistical analysis. Students are also expected to improve their abilities to interpret data by identifying significant features and patterns, use mathematics to represent relationships between variables, and take into account sources of error. When possible and feasible, students should use digital tools to analyze and interpret data. Whether analyzing data for the purpose of science or engineering, it is important students present data as evidence to support their conclusions. 

from A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas (page 51)

Scientific investigations produce data that must be analyzed in order to derive meaning. Because data usually do not speak for themselves, scientists use a range of tools including tabulation, graphical interpretation, visualization, and statistical analysis—to identify the significant features and patterns in the data. Sources of error are identified and the degree of certainty calculated. Modern technology makes the collection of large data sets much easier, thus providing many secondary sources for analysis.

Engineers analyze data collected in the tests of their designs and investigations; this allows them to compare different solutions and determine how well each one meets specific design criteria—that is, which design best solves the problem within the given constraints. Like scientists, engineers require a range of tools to identify the major patterns and interpret the results.

from NGSS Appendix F: Science and Engineering Practices in the NGSS

Scientific investigations produce data that must be analyzed in order to derive meaning. Because data patterns and trends are not always obvious, scientists use a range of tools—including tabulation, graphical interpretation, visualization, and statistical analysis—to identify the significant features and patterns in the data. Scientists identify sources of error in the investigations and calculate the degree of certainty in the results. Modern technology makes the collection of large data sets much easier, providing secondary sources for analysis.

Engineering investigations include analysis of data collected in the tests of designs. This allows comparison of different solutions and determines how well each meets specific design criteria—that is, which design best solves the problem within given constraints. Like scientists, engineers require a range of tools to identify patterns within data and interpret the results. Advances in science make analysis of proposed solutions more efficient and effective.

K-2	3-5	MS	HS
Analyzing data in K–2 builds on prior experiences and progresses to collecting, recording, and sharing observations.	Analyzing data in 3–5 builds on K–2 experiences and progresses to introducing quantitative approaches to collecting data and conducting multiple trials of qualitative observations. When possible and feasible, digital tools should be used.	Analyzing data in 6–8 builds on K–5 experiences and progresses to extending quantitative analysis to investigations, distinguishing between correlation and causation, and basic statistical techniques of data and error analysis.	Analyzing data in 9–12 builds on K–8 experiences and progresses to introducing more detailed statistical analysis, the comparison of data sets for consistency, and the use of models to generate and analyze data.
Record information (observations, thoughts, and ideas).  Use and share pictures, drawings, and/or writings of observations.  Use observations (firsthand or from media) to describe patterns and/or relationships in the natural and designed world(s) in order to answer scientific questions and solve problems.  Compare predictions (based on prior experiences) to what occurred (observable events).	Represent data in tables and/or various graphical displays (bar graphs, pictographs, and/or pie charts) to reveal patterns that indicate relationships.	Construct, analyze, and/or interpret graphical displays of data and/or large data sets to identify linear and nonlinear relationships.  Use graphical displays (e.g., maps, charts, graphs, and/or tables) of large data sets to identify temporal and spatial relationships.  Distinguish between causal and correlational relationships in data.  Analyze and interpret data to provide evidence for phenomena.	Analyze data using tools, technologies, and/or models (e.g., computational, mathematical) in order to make valid and reliable scientific claims or determine an optimal design solution.
	Analyze and interpret data to make sense of phenomena, using logical reasoning, mathematics, and/or computation.	Apply concepts of statistics and probability (including mean, median, mode, and variability) to analyze and characterize data, using digital tools when feasible.	Apply concepts of statistics and probability (including determining function fits to data, slope, intercept, and correlation coefficient for linear fits) to scientific and engineering questions and problems, using digital tools when feasible.
		Consider limitations of data analysis (e.g., measurement error), and/or seek to improve precision and accuracy of data with better technological tools and methods (e.g., multiple trials).	Consider limitations of data analysis (e.g., measurement error, sample selection) when analyzing and interpreting data.
	Compare and contrast data collected by different groups in order to discuss similarities and differences in their findings.	Analyze and interpret data to determine similarities and differences in findings.	Compare and contrast various types of data sets (e.g., self-generated, archival) to examine consistency of measurements and observations.
Analyze data from tests of an object or tool to determine if it works as intended.	Analyze data to refine a problem statement or the design of a proposed object, tool, or process.  Use data to evaluate and refine design solutions.	Analyze data to define an optimal operational range for a proposed object, tool, process or system that best meets criteria for success.	Evaluate the impact of new data on a working explanation and/or model of a proposed process or system.  Analyze data to identify design features or characteristics of the components of a proposed process or system to optimize it relative to criteria for success.

Goals for SEP4: Analyzing and Interpreting Data

from A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas (pages 62-63)

By grade 12, students should be able to 

• Analyze data systematically, either to look for salient patterns or to test whether data are consistent with an initial hypothesis. 
• Recognize when data are in conflict with expectations and consider what revisions in the initial model are needed. 
• Use spreadsheets, databases, tables, charts, graphs, statistics, mathematics, and information and computer technology to collate, summarize, and display data and to explore relationships between variables, especially those representing input and output. 
• Evaluate the strength of a conclusion that can be inferred from any data set, using appropriate grade-level mathematical and statistical techniques. 
• Recognize patterns in data that suggest relationships worth investigating further. Distinguish between causal and correlational relationships. 
• Collect data from physical models and analyze the performance of a design under a range of conditions.  

K-2	3-5	6-8	9-12
K-PS2-2 K-LS1-1 K-ESS2-1 1-ESS1-1 2-PS1-2 K-2-ETS1-3	3-LS3-1 3-LS4-1 3-ESS2-1 3-ESS2-2 5-ESS1-2	MS-PS1-2 MS-PS3-1 MS-LS2-1 MS-LS4-1 MS-LS4-3 MS-ESS1-3 MS-ESS2-3 MS-ESS3-2 MS-ETS1-3	HS-PS2-1 HS-LS3-3 HS-LS4-3 HS-ESS2-2 HS-ESS3-5

A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas (pages 61-63)

Science Practices Continuum - Students' Performance
This tool is a continuum for each practice that shows how students' performance can progress over time. A teacher can use the continuum to assess students' abilities to engage in the practices and to inform future instruction. From Instructional Leadership for Science Practices.

Science Practices Continuum - Supervision
This tool is a continuum for each practice that shows how instruction can progress over time. An instructional supervisor can use the continuum to identify the current level for a practice in a science lesson. Then the supervisor can provide feedback, such as offering instructional strategies to help move future instruction farther along the continuum. From Instructional Leadership for Science Practices.

Potential Instructional Strategies for Analyzing and Interpreting Data
This instructional strategies document provide examples of strategies that teachers can use to support the science practice. Supervisors might share these strategies with teachers as they work on improving instruction of the science practices. Teachers might find these helpful for lesson planning and implementing science practices in their classrooms. From Instructional Leadership for Science Practices.

Bozemanscience Video