Skip to main content
Activity (Hands-On)Grades 9 - 12

Exploring “Typical” Values Using Data with Jupyter Notebook

A screenshot of code showing a dataset of numerical values displayed alongside calculated mean and median values, illustrating how both measures represent the center differently.Students use Jupyter Notebook to explore "typical" values

Students explore the concept of what is “typical” in a dataset by analyzing measures of center (mean, median) and spread (range, standard deviation) using a Jupyter Notebook. Rather than beginning with formulas, the activity starts with a guiding question: What value best represents a dataset? Students first examine simple data and make intuitive judgments before learning formal definitions. Using Python tools such as NumPy and Pandas, students compute statistical measures and observe how these values change when the data is modified. They then visualize distributions using histograms and boxplots to better understand patterns such as symmetry, skewness, and outliers. The activity extends to real-world housing data, where students analyze home prices and recognize how skewed distributions affect the interpretation of mean and median. Through this process, students connect abstract statistical concepts to practical decision-making.

Engineers and data scientists rely on statistics to make sense of complex, real-world data and make informed decisions about the systems, structures, and technologies they design. Before drawing conclusions or making recommendations, they must determine whether the data accurately represent typical conditions or whether unusual values (outliers) could lead to misleading results. Rather than relying on a single statistic, engineers compare measures such as the mean, median, and standard deviation and use graphical representations, including histograms and box plots, to identify patterns, variability, skewness, and outliers. These skills are essential in applications such as analyzing housing prices, optimizing transportation systems, monitoring environmental conditions, and evaluating engineering designs.

After this activity, students should be able to:

  • Distinguish between typical and atypical (outlier) values.
  • Use Python (NumPy, Pandas) to analyze datasets.
  • Interpret histograms and boxplots to understand data distribution.
  • Explain when mean vs. median is more appropriate.
  • Apply statistical reasoning to real-world data (housing prices).

More Like This