Objective

By the end of this lesson, the student will understand how to create and interpret box plots (box-whisker plots) using bee pollen count data collected through citizen science. They will learn how to visualize data effectively and draw conclusions from their findings.

Materials and Prep

• Paper and pencil for note-taking and calculations
• Access to a computer or tablet with internet for research
• Data collection sheet for bee pollen counts (can be created on paper)
• Graphing tools (can be done on paper or using an online graphing tool)
• Basic understanding of statistics (mean, median, mode, range)

Activities

• Research Pollinators:

  The student will start by researching different types of pollinators and their importance to ecosystems. They will create a brief report on how bee pollen counts can indicate the health of pollinator populations.

• Data Collection:

  The student will participate in a citizen science project by collecting data on bee pollen counts in their local area. They can observe flowers in their garden or local parks and record the number of bees visiting each flower over a set period.

• Creating a Box Plot:

  Using the data collected, the student will learn how to create a box plot. They will calculate the median, quartiles, and identify any outliers in their data set, then graphically represent this information using a box plot.

• Data Analysis:

  The student will analyze the box plot to draw conclusions about the bee pollen counts. They will discuss what the data reveals about the health of local pollinator populations and how this relates to environmental factors.

Talking Points

• "A box plot is a great way to visualize data because it shows the median, quartiles, and any potential outliers, giving us a clear picture of the data distribution."
• "Citizen science projects allow us to contribute to real scientific research. By collecting data on bee pollen counts, we can help scientists understand the health of pollinator populations."
• "The median is the middle value in a data set when it is ordered from least to greatest. It helps us understand the central tendency of our data."
• "Quartiles divide our data into four equal parts, giving us insight into how our data is spread out. The interquartile range (IQR) helps us understand variability."
• "Outliers are data points that fall far outside the range of the rest of the data. Identifying them can help us understand unusual occurrences in our data."