Students often struggle with how to analyze and present the data they obtain in an experiment. While many schools offer courses in statistics or in computer spreadsheet and graphing applications, these are rarely prerequisites for science courses.
In your science class, spend some time discussing data analysis at the beginning of the year to help your students understand what their data means and how best to present what they learn from their experiments. There are multiple NGSS science and engineering practices that address data collection and analysis: analyzing and interpreting data, using mathematics and computational thinking, and arguing from evidence. Here are some important topics to cover with your students as they gain experience analyzing data.
Mean, median, and mode
Make sure students know the difference between these and when it is appropriate to use each.
- The mean is the number obtained by dividing the sum of all the values in a data set by the total number of values.
- The median is the middle value when all the values are arranged in order.
- The mode is the most frequent value.
Typically, the x-axis represents the independent variable, and the y-axis represents the dependent variable. The mnemonic DRY MIX, for “dependent, responding, y-axis” and “manipulated, independent, x-axis,” can help students remember this point. Students should also know about different types of graphs and which types of data are best represented by each. Scatter plots, bar graphs, and line graphs are the most commonly used graphs in science. Students should also be familiar with pie charts.
Additional Reading: The Basics of Graphs and Charts
All students should know that this is an indication of how tightly the data points are clustered around the mean. Advanced students might also learn how to calculate this value.
All students should understand that sample size affects the reliability of the data (with larger sample sizes increasing confidence in the data).
Discuss and revisit these basics to boost students’ understanding of their experiments and their analysis and dissemination of results. In addition to helping them with their own analyses, the knowledge and practice will help them better understand the significance or judge the validity of data presented in other people’s research.