Analyzing and Interpreting DATA
"...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."
Introduction to Analyzing and Interpreting Data
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. 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.
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.
Spreadsheets and databases provide useful ways of organizing data, especially large data sets. The identification of relationships in data is aided by a range of tools, including tables, graphs, and mathematics. Tables permit major features of a large body of data to be summarized in a conveniently accessible form, graphs offer a means of visually summarizing data, and mathematics is essential for expressing relationships between different variables in the data set (see Practice 5 for further discussion of mathematics). Modern computer-based visualization tools often allow data to be displayed in varied forms and thus for learners to engage interactively with data in their analyses. In addition, standard statistical techniques can help to reduce the effect of error in relating one variable to another.
Students need opportunities to analyze large data sets and identify correlations. Increasingly, such data sets—involving temperature, pollution levels, and other scientific measurements—are available on the Internet. Moreover, information technology enables the capture of data beyond the classroom at all hours of the day. Such data sets extend the range of students’ experiences and help to illuminate this important practice of analyzing and interpreting data.
Key Features
The purpose of working with data is to answer a question or solve a problem.
A range of tools are used to analyze and interpret data. (tables, graphs, math, computers, statistics, etc.)
Analyzing and interpreting data is related and connected to all other scientific practices. It would be meaningless if separated.
It’s not a solo act. Requires communication, argumentation, and collaboration.
What it is NOT
Making graphs or other representations of data without connecting to a question or clear scientific purpose.
Answering decontextualized questions about data.
Making and recording measurements without a clearly understood question or research purpose.
K-12 Progressions for Analyzing and Interpreting Data
SEP_AID.pdfInstructional Strategies for Analyzing and Interpreting Data
Source: Instructional Science Leadership
After an investigation, ask each group of students to briefly state a pattern they see in the data. Provide sentence starters such as “As the amount of ________ increases…” and “We saw that changing _______ caused…”
Provide written steps for students to follow to scaffold analyzing complex data tables. For example, students might be asked to first state how many trials were conducted, then asked what pattern they see in the first column of the data table. As student capability with finding the patterns in data improves through the school year, slowly remove the scaffold.
Ask students to vote (thumbs up/thumbs down) whether they agree with a fellow student’s interpretation of the patterns in data.
To practice figuring out patterns in the data give groups of students a data table and sentence strips with various statements about the patterns in the data. Have students decide whether each statement is accurate or inaccurate based on the data table.
Have groups of students compare and contrast their data tables. If differences exist in the data, ask student hypothesize about why these differences exist. Have students make a plan to reduce sources of error in future iterations of the investigation (i.e. dropping a ball from the same height, having the same students operate a stopwatch through the investigation, etc.).
Ask students to graph their data to visually represent the patterns in the data. Provide checklists for students to use to ensure their graphs contain key components, such as labels on the axes and a title.
Conduct a gallery walk for students to view and critique each other’s data tables or graphs. Encourage students to use sticky notes to ask questions and provide feedback about how well their data tables show the patterns in the data. Give students time to use the feedback to improve their work.
Model for students how to construct a graph. Talk about what decisions must be made when creating a graph (e.g. bar graph vs. line graph) and the reasons for one choice or another. Point out aspects of graphs that enable other to comprehend patters in the graph (e.g. reasonable intervals on the axes).
Hang posters in the classroom with examples of different types of graphs (bar, line, etc.) that students can reference as they decide what type of graph to construct and as they make their graphs.
After students construct a graph for data, ask them to defend their choice of that type of graph. Facilitate a discussion about the differences in how each graph type shows the patterns in the data.
Have students write 1-2 sentences that summarize the pattern(s) in a graph. Provide sentence starters such as “My graph shows...” and “Over time, plant A…”.
Learn more about Analyzing and Interpreting Data
Bozeman Science Video - Practice 4 - Analyzing and Interpreting Data
Wonder of Science Organizer: Analyzing & Interpreting Data - Google Draw or PDF
Webinar: Analyzing and Interpreting Data
Science Practices Continuum - Tool for guiding and evaluating science-practice based instruction
Instructional Resources:
CODAP - Common Online Data Analysis Platform
HHMI Data Points - Interpret and discuss figures from primary literature
Our World in Data - data focussing on the powerful changes that reshape our world
Data Nuggets - free classroom activities, co-designed by scientists and teachers.
Information is Beautiful - Data, information, knowledge: we distill it into beautiful, useful graphics & diagrams.