Advice for Students
- Keep the experiment fairly simple, but analyze it really well.
- Cite as you go. Having to locate your sources afterwards is a waste of time and a potential academic honesty issue.
- Don't underestimate your uncertainty but also don't overstate it. Make sure you can justify the uncertainty that you are using for your measurements
- Explain what the slopes and y-intercepts of your graphs mean. A mathematical relationship is not enough.
- Evaluate your results. Do you have good/valid results? How did systematic errors affect your results?What limitations are there in how you collected/analyzed your data? How do the limitations affect the validity of your results? How do you know?
- Cite as you go. Having to locate your sources afterwards is a waste of time and a potential academic honesty issue.
- Don't underestimate your uncertainty but also don't overstate it. Make sure you can justify the uncertainty that you are using for your measurements
- Explain what the slopes and y-intercepts of your graphs mean. A mathematical relationship is not enough.
- Evaluate your results. Do you have good/valid results? How did systematic errors affect your results?What limitations are there in how you collected/analyzed your data? How do the limitations affect the validity of your results? How do you know?
Rubric
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This is the rubric that I use with my students.
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Checklist
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I don't typically use this checklist anymore, but some may find it useful:
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Not a checklist
InThinking has a really nice group of suggestions for how to approach each section of the IA here.