Analyzing data in K–2 builds on prior experiences and progresses to collecting, recording, and sharing observations. 

• 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).  

• Analyze data from tests of an object or tool to determine if it works as intended. 

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.  

• Represent data in tables and/or various graphical displays (bar graphs, pictographs and/or pie charts) to reveal patterns that indicate relationships. 

• Analyze and interpret data to make sense of phenomena, using logical reasoning, mathematics, and/or computation.  

• Compare and contrast data collected by different groups in order to discuss similarities and differences in their findings.   

• 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. 

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.