I just realised that even though I know how to perform an independent samples t-test or a Mann whitney test, I am not sure how their results should be reported in a paper.
I was given this study to read in preparation for a Research Methodology class but it does not report the "easy" tests, so I wonder.
Edit in response to the comment:
I mean reporting according to strict scientific guidelines. I suppose there is a rule, similarly to when eg we report normally distributed variables we mention the mean and the SD.
Edit number two 🙂
I am sorry I didn't realise I wasn't specific. My orientation is medical research so I am primarily interested in knowing what is the best way to present data in papers that result from medical studies. The class I am taking right now is more general though (the article was from a study from the Law school) so it did not occur to me that this was a detail I should have mentioned in the first place.
So lets assume I checked if x_bubblenephrine is different between say, a group of people who have Y-itis and a group who of people who do not. Say that I got p>0,005. Is there a "correct way" per se to report this? Or I can get away with "there was no difference between the two groups (p>0,05)"?
Best Answer
Stats can be reported in many modes (e.g., class hw, conference presentation, journal article) and research fields. The general rules vary depending on where and how you are reporting them.
Often times, when people want to see stats reported in a certain way, they will tell you. For example, when researchers submit a manuscript to a journal, that specific journal should have its own guidelines on how to format the paper.
Other than that, you just have to study and understand stats until you kind of get an idea of what is and isn't important in the context that you are in. Like the other comments, it depends a lot on the audience.
I can't list out everything, but a few examples that I believe in:
More numbers should be reported on a paper than a presentation.
Report actual p-values whenever possible, not just p>.05 or p<.05 (but if p<.001 then just say p<.001)
standardized effect sizes (like r or r^2) is more useful when your study involves latent variables (i.e., variables without clear units, like happiness score on a questionnaire).
Confidence intervals are more useful when you are dealing with manifest variables (i.e., variables with meaningful units, like height in inches)