When is is hard to be precise and how does this affect the confidence in your results? What did you do about this?
When can you rely on "known" data to match up with and when do you need to generate you own? What is the difference?
Multiple observations and experiments give you extra information so you can make good conclusions at the end of each case. For instance, in the soil lab we not only described the general appearance, we also took notes about the pH, conductivity, and water absorption. If we only wrote observations about the conductivity and pH, we would have to make final conclusions about the case based on only those two pieces of evidence. Making a final conclusion with only two sample results would be a lot less informational and a lot less precise. Multiple observations give you better, more precise information, and a more improved conclusions. More information leads to conclusions based on multiple sources of information instead of just the appearance of a cup of dirt.
It's extremely hard to be precise in a lab when the tools you're using are completely foreign to you, and when the answer you're getting is an inference. When you know your answer is a guess, it affects the confidence you have because you can't be certain if it's correct or not. When we used the caliper in the bullet analysis case, it was harder to be precise, and so when we compared our data with different types of bullets we kept in mind that the information we had might not be exact. It was also hard to be precise when we were measuring the lands and grooves. We had to decide which were the lands and which were the grooves. Doing that wasn't always easy, sometimes the lands and grooves looked and felt almost exactly the same. What we did in situations like this, is we kept in mind that our answer might be off by a little. That in mind is was easy to find the correct answer by adding or subtracting a tenth of an inch for the calipers and easy to find the lands and grooves on the charts.
You can rely on "known" data when you can use data you come up with to justify the information. You need to generate your own data when the "known" data isn't from a reliable source or when the information is a known inference, and not known information. The difference is that when you have information given to you as a part of the case, you should use it. But when you don't know where the information came from or it's not "known" data you might not want to use it as it might not be true data.
