Students investigate whether packaged foods give consumers a fair picture of what is inside by collecting and analyzing their own weight data from easy-to-buy items and comparing it to package labels. They use dot plots, histograms, box plots, mean, median, interquartile range, and outlier analysis to compare distributions and make evidence-based fairness claims. Through collaboration, critique, and feedback from an ice cream scientist, students connect Algebra 1 statistics to quality control, consumer trust, and real decisions in the marketplace. The work leads to a public showcase panel and data walk where students defend a clear recommendation for consumers with real measurements and statistical evidence.

Students will collect and measure real packaged food samples, then represent the data with dot plots, histograms, and box plots to compare labeled and actual weights. They will calculate and interpret mean, median, and interquartile range for two or more data sets, using center, spread, shape, and outliers to decide whether a package label gives a fair picture of what is inside. Students will build and revise a clear evidence-based consumer recommendation for their showcase panel after critique, peer feedback, and discussion with the ice cream scientist. They will strengthen collaboration and communication by sharing measurement tasks, defending statistical claims during the data walk, and reflecting on how their thinking changed over the project.

Students will create measurement logs, shared class data tables, and draft dot plots, histograms, and box plots for easily purchased packaged foods such as single-serve ice cream cups, chip bags, or candy packs, comparing measured weights to package labels. Midway through the project, each group will produce a critique-ready data display and a short fairness claim that uses mean, median, interquartile range, and notes any outliers. The final product will be a “fair or not fair” showcase panel that combines graphs, summary statistics, and a concise consumer recommendation based on whether the labeled weight gives a fair picture of what is inside. For the Scatter, Slice, and Decide Showcase, students will also prepare a brief oral defense using real packaged samples to explain and defend their final verdict to families, staff, and the ice cream scientist.

Set up a “Weigh and Wonder” table with sealed, easy-to-buy items students can measure in class, such as fun-size candy bags, single-serve chip bags, snack cups, or granola bars, and have partners predict which packages will be closest to or farthest from the labeled weight. Students quickly weigh several samples, record the actual weights, and post first-notice patterns and surprises on a class chart focused on fairness, consistency, and possible outliers. Follow with a short whole-class discussion that introduces the driving question about whether package labels give a fair picture of what is inside. End the launch by previewing that an ice cream scientist will help them connect these weight distributions to real quality-control decisions.

Host a “Scatter, Slice, and Decide” data walk where families, staff, and the ice cream scientist rotate through student showcase panels and inspect the actual packaged samples students tested. Each group presents its “fair or not fair” verdict by explaining dot plots, histograms, or box plots, comparing mean, median, and interquartile range, and pointing out any outliers that changed the claim. Include a short audience feedback form asking visitors whether the group’s evidence was convincing and what questions they still have about label fairness. Close with a brief whole-group share-out and reflection circle in which each student names one statistic they used, one claim they revised, and one way they grew in both mathematics and collaboration.