Science.7.1.F

Previous Next
The student is expected to construct appropriate tables, graphs, maps, and charts using repeated trials and means to organize data;
1666 TEKS header image

Knowledge and Skills Statement

Scientific and engineering practices. The student, for at least 40% of instructional time, asks questions, identifies problems, and plans and safely conducts classroom, laboratory, and field investigations to answer questions, explain phenomena, or design solutions using appropriate tools and models.

Accuracy and precision are related concepts that are often confused.  Accuracy and precision are both ways to measure data. Accuracy measures how close results are to the true or known value, often described by the percent error. Precision measures how close results are to one another, often measured by the standard deviation. Precision includes both repeatability and reproducibility.  To make data repeatable, all variables, such as the instrument, operator, and environmental factors, are kept constant.  For data to be reproducible, the same results must be obtained by different operators, on different instruments, and at different times. The image below shows the relationship between accuracy and precision.

A chart that shows four dart boards as models of accuracy and precision. The top left dart board shows five darts in the center of the board and is labeled as both accurate and precise. The top right dart board shows six of the eight darts circling the center of the dart board, but not on the bullseye. It is labeled accurate, but not precise. The bottom left dart board shows five darts all grouped together to the lower left of the dart board. It is labeled precise, but not accurate. The bottom right dart bo
Image Source: File:Accuracy and Precision.svg - Wikimedia Commons


Accuracy and precision combine to produce the margin of error (+/-). The accuracy and precision of data are often stated implicitly using significant figures. When the data are graphed, accuracy is seen in the amount of horizontal shift in the data, and precision is seen in the dispersion or spread of the data. 

detectable events that are observed through the senses or technology; can be explained through scientific laws, ideas, principles, and theories

Research

Hunter-Thomson, Kristin. “Data Literacy 101: How Do We Set up Graphs in Science?” Science Scope 42, no. 2 (2018): 78–82. https://www.jstor.org/stable/26611838

Summary: Beginning with considering the type of data provided and the information needed to convey, "How Do We Set Up Graphs in Science?" shares how to determine appropriate graphs for communicating specific data with various audiences.  A variety of graph types are explained, as well as the established criteria for each graph.