I'm thinking about how the general public interprets scientific claims published and shared through popular outlets. It's very common for people who haven't even read past the headline to dismiss the study by saying "that [whatever findings were reported] is just correlation." But biomed and treatment efficacy literature for instance is rarely just slapping a correlation matrix together and calling it a day. The analyses typically do try to differentiate between causation and correlation in their experimental designs, though of course that doesn't mean the evidence is persuasive, that the sample is representative, etc. There is an argument that this is kind of a faux-scientific way of attacking a study without actually engaging with its logic of argumentation.

TLDR: Is correlation ≠ causation overused or improperly used to reject potentially important scientific findings that one simply disagrees with?

Hi everyone, I am building a mixed-effects model to investigate how several explanatory variables affect an outcome. Based on my exploratory data analysis, I suspect that certain combinations of variables have three-way interactions.

For example, suppose I have four explanatory variables: A, B, C, and D. I suspect there is a three-way interaction between A × B × C, but I haven't found clear evidence suggesting interactions involving D.

My question is: If I include the A × B × C interaction in my model, do I also need to include other possible three-way interactions, such as A × B × D, A × C × D, and B × C × D? Or is it reasonable to include only the interactions that I have reason to suspect based on exploratory analysis or prior knowledge?

I'd also appreciate advice on the statistical implications of selecting interaction terms based on exploratory analysis, as I'm concerned that including only interactions that appear significant or interesting in the data might constitute cherry-picking.

Thanks in advance for any suggestions or references!

Hello!

I'm working on my uni project and have used Spearman test in it. How can I create the visualisation for the data? Preferrably online and free. I tried to search some sources, but could find only those who calculate the results, but I need a visualisation

Hello everybody,

I am relatively new to statistics as a whole but now need to calculate the a priori sample size for a non inferiority study with a predetermined ni-margin, alpha, power and the results of a pilot study given. The metrics are not normal distributed and therefore as far as I know most online calculators are not applicable (or because of the study design as is the case in g power). I consulted ai for an answer and was pointed to calculating the sample size via R but I have never done this before and don't want to calculate it solely based on the code of ai.

Is there any other option, a tutorial or any code I can work with which isn't done by ai ?

Thank you for your help.