The conclusion is where you discuss whether your hypothesis was correct or not, what that means and critique your experimental procedure and results.
The Conclusion Must Include:
-Your hypothesis restated, and then directly concluded upon. This means you must Claim whether the data you have collected supports or does not support your initial hypothesis.
-Support for your conclusion statement using the Evidence from your data section that you are using to make your conclusion. These are the facts that you are basing your argument on. Without them you are basically stating an opinion.
-A discussion of your experimental results. This is where you show that you can intelligently discuss the experiment that you just performed. You should be talking about what the results mean, and the Reason why your evidence backs up your claim. This involves you making connections. You should be looking back at your introduction and seeing what we knew about the ideas involved in this lab before doing this experiment, and explaining how doing this lab has either strengthened, changed, or furthered any of those understandings. You can also make connections to experiences you may have, or to (properly cited) outside research done after doing the experiment. You can discuss whether the results you encountered were a realistic expectation for what will always happen with this experiment. Or you could talk about the reasoning for why the results came out the way they did.
In some labs you may be given lab questions that will be included here or replace this section or ask you to work your answers into this section.
-An explanation of possible sources of error AND how those errors would change the results of the experiments. I expect you to completely discuss at least two significant sources of error.
There are two types of error to look for, measurement error and systemic error. Measurement error is in every lab to varying degrees. This can be broken down into two issues, accuracy and precision. Error in accuracy would be saying that your measuring device gives results that are not near the actual value. An example would be a spring scale where the spring has been stretched out from overuse and now it gives values that are much larger than the actual force being applied. We generally trust our equipment to be accurate, but you cannot rule this out. Precision error is due to the limitation in any of our instruments that they are only so precise, and therefore can only go to so many decimal places. For example our meter sticks are marked down to the millimeter. This means that any measurement that we make is limited to +/- .0005m. Meaning you could be off by as much as a half a millimeter in either direction when you take that measurement simply because that it a limitation of the device. (note neither of these are "human error" they are measurements done by humans, but are limitations in our measurement devices)
Systemic error is error caused by us making assumptions within our procedure. For example, If we were trying to calculate acceleration for a falling object, and to calculate this we assumed that we dropped the object from rest. It is possible that in trying to drop from rest that you gave it a slight push up or down and so this assumption is incorrect. This would affect the results but you cannot eliminate this completely by being more careful. (these are not mistakes, but rather acknowledgments that we made assumptions that may not be 100% accurate)
- An outline of a follow up experiment to the one you just did. Explain briefly what you would test and how it would connect to the concepts of this lab. This should allow you to learn more about the same topics.
Helpful Hints:
- If you want you can use, "The hypothesis that ______ has been found to be (supported / not supported) by the data taken in this experiment." as your opening statement. Just be cautious of using this with longer hypothesis as it can become a pretty awkward sentence.
- One way to think about sources of error is to say to yourself, "What are some things that we assumed to be 100% true that may not have been?" For example: If you were dropping something in the experiment and you assumed it dropped from complete rest, it is possible that you let it go and pushed it in one direction or another that would have given it some initial velocity.
Common Mistakes:
-Concluding that the data was "kind of supported". Don't straddle the fence. There are often cases where the data could be argued to support or reject the hypothesis, so pick a side and argue it.
- If you aren't including numbers from your data section, you aren't supporting your conclusion properly. "Look at the data above" doesn't count.
- Mistakes are not errors. Mistakes can be fixed by being more careful, you would have to change your procedure to fix a source of error.
- NEVER SAY HUMAN ERROR. There are errors that occur because measurements are not exact due to people taking the measurements, even when they are taking them to the best of their ability. We discuss this as error in the precision of the instrument and the uncertainty of the measurement. If we had a more precise instrument then a human could get a better reading. If the measurement is obviously wrong because of a "human error" then it is a mistake, because you can go back be more careful and get a better result. If this is the case you are allowed to discount this (outlying) data.
- A follow up experiment is not, "Do the same experiment over again but have machines do it so the measurements are exact." (you would be surprised how many times I have read this in a lab) This shows that you are not thinking about what you've learned and how you can increase that knowledge.