LabXchange is excited to announce that our Data Science–Driven Science Education (DSDSE) project has published a new learning pathway, "Experimental Design, Data, and Variables," which introduces the critical concept of experimental design and explores its importance within the field of data science.

Aimed at high school educators and learners, the resources within this pathway have been carefully designed to align to Next Generation Science Standards (NGSS) and AP standards, ensuring that they are highly relevant to secondary school curricula across the United States and beyond.

Below, learn more about the new pathway and check out a handful of the new learning resources that feature in it.

Experimental Design, Data, and Variables

Researchers are always working on experiments, and robust experimental design is necessary for them to be able to trust their conclusions. The "Experimental Design, Data, and Variables" pathway covers fundamental concepts in experimental design that are related to data science, such as asking testable research questions, independent and dependent variables, generating hypotheses, designing positive and negative controls, data types, determining correlation versus causation, and sampling.

Learning Objectives

In this pathway, students will learn to:

1. Generate testable research questions and hypotheses, and classify variables as dependent or independent.
2. Develop questions and experiments designed to reduce bias & display the data in a clear, ethical manner.
3. Recognize patterns and confounding variables which can affect the story told by the data (such as correlation vs causation).

Featured Resources

This pathway includes a curated selection of texts, infographics, images, and question sets. In addition, eight of the learning resources within this pathway are accompanied by student worksheets, which are printable worksheets designed to enhance students' learning through relevant questions and charts.

Positive and Negative Controls (infographic + worksheet)

Have you ever been asked to include controls in a science experiment but aren’t sure why? This infographic defines experimental controls and explains how including them in experiments ensures the reliability and validity of results.

Independent vs Dependent Variables (question set)

Variables, including independent variables and dependent variables, form the foundation for understanding cause-and-effect relationships within research. This question set explains the basics before asking you to identify the independent and dependent variables in different experimental scenarios.

What Is Bioinformatics? (text + images)

This article defines bioinformatics and provides an overview of a viral genomics research project that heavily uses bioinformatics methods.

Identifying Misleading Uses of Data (text + images + worksheet)

Data can be misrepresented—let's explore a few of those common misrepresentations. In this article, learn some strategies to avoid being deceived by data or unintentionally deceiving someone using data.

About the Data Science–Driven Science Education Project

With generous support from the U.S. Department of Defense (DoD) STEM, the DSDSE project was launched in 2023 as an ambitious initiative aimed at educating the next generation of learners on the the incredible real-world importance of data science and data literacy.

The project will provide sustainable, long-term data science resources for high school students and educators that will augment national digital literacy by integrating data science with existing high school STEM curricula and building educator capacity to confidently lead students in data science explorations.

Learn more in the initial DSDSE project announcement and visit the DSDSE project page to stay up to date on what's coming next. Plus, check out the two previously published pathways in the project:

• Summary Statistics and Probability
• Data Visualization