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Scrolling for the Truth

Are today's teens healthier or less healthy than teens in the past?

Isometric phone data analysis

Creative Artifact

Students will create a statistical data visualization and an accompanying social media post that deeply analyzes results from the Youth Risk Behavior Survey and identifies what we can—and cannot—validly infer about teen health from these data.

Written Commentary

To craft their posts, students will engage in a statistical inquiry cycle: formulating questions, gathering relevant survey data, and analyzing and interpreting patterns across years and subgroups. They will apply their understanding of simulations, confidence intervals, and survey methodology to evaluate how strongly the data support different statistical claims, accounting for uncertainty and the limitations of the data.

Exhibition

The class will hold a “doomscrolling session” where they evaluate the credibility of one another’s posts, then use those posts as a jumping off point to discuss salient local teen health concerns.

Implementation Notes

Credit Eligibility

  • nth root symbol

    Math

Prerequisites Needed

Exposure to early high school statistics standards

Modular Suggestions

A unit within a course tied to Statistics, Data Science or Quantitative Reasoning

TLE-Based Semester/Full-Year Course Suggestions

Applied Statistics: Pair with Communities & Opportunities, making this unit the second in a one-semester course.

NOTE: Springpoint is developing two additional units that can ultimately serve as a one-year course.

Standards Addressed

The Statistics TLEs use the GAISE (Guidelines for Assessment and Instruction in Statistics Education) framework as its primary set of statistical standards. GAISE is a nationally recognized framework developed by the American Statistical Association that organizes learning around the following four phases of the Statistical Investigation Cycle:

  • Formulate Statistical Investigative Questions
  • Collect/Consider the Data
  • Analyze the Data
  • Interpret the Data
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