Students will engage in a hands-on exploration of data visualization techniques to understand how outliers affect data sets. By analyzing real-world data, students will create visual representations such as box plots and histograms, allowing them to observe differences in shape, center, and spread. This project-based approach encourages students to draw conclusions about the significance of outliers and their impact on data interpretation, fostering critical thinking and data literacy skills. Collaborating with a local university's statistics department, students will gain insights into real-world applications, enhancing their understanding through mentorship and expert guidance.

Students will explore how to visually represent data sets using graphs and charts, focusing on identifying outliers. They will analyze how these outliers affect the shape, center, and spread of the data. Through hands-on activities, students will interpret real-world data, drawing conclusions about the impact of extreme data points on overall trends. By the end of the lesson, students will articulate the significance of outliers in data analysis and apply this understanding to various contexts, enhancing their ability to collaborate with peers and experts in the field.

Students will create a series of data visualizations, such as box plots and histograms, using a real-world dataset. They will annotate these visualizations to highlight the shape, center, and spread of the data, and identify any outliers. Finally, students will compile their findings into a digital presentation, using graphs and charts to explain how the outliers affect the overall interpretation of the data.

Begin the lesson with a hands-on activity where students use a simple, relatable data set, such as their classmates' shoe sizes. Have them create a dot plot on a whiteboard or large paper. Introduce an outlier by adding an exaggerated shoe size and ask students to observe and discuss how this affects the shape, center, and spread of the data. Encourage them to use sticky notes to annotate their observations directly on the plot, fostering a collaborative exploration of the impact of outliers.

Students will create a visual presentation using graphs and charts to showcase their analysis of a real-world data set, highlighting the impact of outliers. They will present their findings to the class and invited university mentors, explaining how the shape, center, and spread of the data set change with and without the outliers. Peers and mentors will engage in a gallery walk, providing feedback and asking questions to deepen understanding. This exhibition will culminate in a reflective discussion on how data visualization aids in interpreting data effectively.