Food for thought:
Over the years, teachers and faculty have adopted topics in Wikipedia as a way to practice Open Pedagogy with reusable assignments. It provides a real life experience to evaluate information and edit with new or contradictory evidence.
The pro and con arguments that apply to the crowd-sourced Wikipedia often apply to citizen science crowd-sourcing, too.
- Honest and objective data collection can bring tremendous insight into an issue.
- Topics can be distorted aggressively by people with an agenda.
- Normal confirmation bias can bias the observations without the observer realizing it.
Confirmation bias examples:
- Discounting: Counting fewer birds where fewer birds are expected.
- Overcounting: Counting more birds when the birds are noisy, hyperactive, or expectations are for more birds.
- Propaganda: Cherry-picked, exaggerated, or faked data provided to make the results appear more supportive of a theory (such as there are fewer/more) birds than before.
- Hubris: Mis-identifying a bird because you "know" what that bird looks like, and so you don't need to look it up.
Keep in mind:
- Sometimes a large number of data points can give you a general idea of something that is otherwise difficult to determine. For examples, bird populations or butterfly migration routes.
- One hopes the sheer number of data points will minimize the effects of collection errors, but that is not always true.
- GIGO (Garbage In-Garbage Out ): The data is only as good as the method used to collect and record the data.
- Whenever possible, data should be confirmed with more rigorous research by authorities in the fields studied.
