Reflect on this:
The results of my experiment on walking speed were unexpected. As we discussed in class, in my data the people walking in the rain were the slowest. People walking on one sunny day seemed a little faster, but the difference was not large enough to call it statistically significant. The other sunny day produced the fastest walking speeds.
So what happened? Our hypothesis was that people would walk faster on a rainy day than on a sunny day, but our data did not support this hypothesis. In science its not uncommon to get unexpected data. When that happens, we need to determine whether our original hypothesis is wrong, or whether there were faults in our experimental design, such as factors or variables that we didnt count on. If there were, we might adjust our experiment and try again.
Think about my experimental design: I decided to measure walking speed on a city street on one rainy day and two sunny days. My independent variable was rain. But in the real world, is that the only thing that might make people walk faster or slower?
In about 100 words, list and discuss as many additional factors as you can think of, which might impact walking speed. For example, should I be sure to conduct my observations on the same day of the week to avoid any differences in pedestrian traffic?
As always with reflections, its not the number of words that matters, its the effort you put into the exercise.