GCCC Hypotheses to Determine Effect of Aspect on Different Variables Discussion

Having Trouble Meeting Your Deadline?

Get your assignment on GCCC Hypotheses to Determine Effect of Aspect on Different Variables Discussion  completed on time. avoid delay and – ORDER NOW

Do My Paper

The discussion section is where you get to talk about what you found in your experiment, and what it means. The introduction and the discussion section are like bookends, and they should complement each other nicely. If you have written a good introduction, the discussion section is a breeze. At the end of the intro, you should have laid out your hypothesis and prediction for the experiment. The prediction is often of the form

If the hypothesis is correct, then x should happen in my experiment

This makes the discussion easy. Did x happen? If so, great. You have supported your hypothesis (note that you haven’t proven your hypothesis – there could be another hypothesis that would lead to the same result). Did x not happen? Even better! You have proven your hypothesis to be flawed (unless you have a power problem – more on that later).

Start your discussion by summarizing your findings IN ENGLISH. No statistical talk here. Do not talk about findings that you didn’t report in your Method section. Then interpret your findings. Are they consistent with your hypothesis or not? Do they support or refute your hypothesis? If you have multiple findings, you may want to devote a paragraph to the discussion of each. This statement of results and implications is the bare bones of a discussion.

A good discussion will then go on to give some context to the results. Are they consistent with other research in the field? Is there a discrepancy in the literature that your study helps to clarify? Or do you add to a growing body of evidence for something?

If you have some concerns about your interpretation, now is the time to voice them. If you got null effects, do you think it could be because you don’t have enough power (enough subjects or enough trials). Note that power can only be a possible explanation if your results are at least going in the right direction. If you used similar numbers of subjects to others who have found effects, than power is not likely the problem. If you have correlational data, you would want to caution your reader that you are unable to make causal interpretations.

Finally, does your study raise new questions? If your findings were unexpected, can you think of a hypothesis to explain them? Is there any support in the literature for this new hypothesis? Could you propose a way to test your new hypothesis that could be used in future research?