Background
Our teens received an introduction of basic sampling techniques by LSU Environmental Sciences doctoral student, Brian Matherne. They learned about the importance of being consistent in measurement and using proper units. Each student took turns weighing their worms on a scale and measuring the length with a ruler.
After the teens plotted their data in side-by-side graphs on the board, a group member had to explain their findings. The students quickly identified common trends and discussed alternative ways of visualizing their findings. The importance of sample size to account for variance was evident, in particular with regards to how stretchy earthworms tend to be.
Students also learned about how wet and dry weights of samples tend to differ. The teens concluded that using the wet weight of their worms likely influenced their findings. This drove home the message about controlling for a non-sampling error. Likewise, group members were able to see how sampling error was created when only investigating a small population size.
This event taught the following key scientific concepts: careful data collection practices, appropriate sample size, data visualization techniques, and effective scientific communication strategies. Earthworms are a fun and cost-effective sampling subject. Not only were teens engaged with the hands-on activity, but they asked a lot of questions about the ecological importance of their subjects.
Materials
- Live earthworms from fishing bait or garden section of your favorite shop
- Rulers with units in centimeters
- Kitchen or lab scale with units in grams
- Trays for sorting
- Weigh boats or small condiment cups to keep track of individuals
- Dry erase/poster board and markers for graphing
Methods
- Divide students into small groups and give each group a container of earthworms
- Each group takes turns at a station to count, weigh, and measure their worms
- Data are plotted on the board
- One group member must explain their findings to the audience
- Compare and contrast the different datasets
