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how well each meets specific design 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. logical reasoning, mathematics, and/or computation. analyze and characterize data, using digital tools when feasible. 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