Analyzing & Interpreting Data

NGSS  SEP 4. Analyzing and Interpreting Data

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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 tabulationgraphical 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.

Interpreting graphs

Issues to discuss 

Introductory Collaborative Investigation
  • Input your height in cm
  • Input your foot length in cm.  You can determine your foot length by entering your size and converting to cm using Wolfram Alpha.
  • Graph height vs.foot length
Analysis and Interpretation of data
Additional Resources
NGSS Progression

Middle School
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.
  • 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.
  • Apply concepts of statistics and probability (including mean, median, mode, and variability) to analyze and characterize data, 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).
  • Analyze and interpret data to determine similarities and differences in findings.
  • Analyze data to define an optimal operational range for a proposed object, tool, process or system that best meets criteria for success.
High School (9-12)
  • 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.
  • 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.
  • 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, sample selection) when analyzing and interpreting data.
  • Compare and contrast various types of data sets (e.g., self-generated, archival) to examine consistency of measurements and observations.
  • 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.