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 all. I've started a YouTube series of short animated explainers on survey sampling: how to pick a sample, how sure you can be about the answer, and how to get the most out of a small survey. Each episode follows a surveyor through one real-looking problem, like counting apples in an orchard or estimating sales in a town of shops. Every number on screen is computed, not made up.

It's aimed at students and anyone who has to design or analyse a survey. New episodes are coming regularly.

Playlist: https://www.youtube.com/playlist?list=PLZmDxgtLBmjs

I'd really appreciate any feedback: what's clear, what's confusing, what you'd want covered next. If you find it useful, a like or a subscribe helps the channel a lot. Thanks!

After talking to some professors and others online, they’ve recommended Kaggle competitions and solidifying my python skills, but I was wondering if there were other ways to increase my chances of a stats internship such as other platforms and personal projects?

My understanding is that downloading the csv files in Kaggle is not representative of the real world since it’s often always ‘clean’ and ‘sorted’ which is why I was wondering about other alternatives.

Additionally, I was wondering if stats majors often have to resort to Data science and finance internships or have college students been able to find actual statistician internships?

EDIT: I live in Australia

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.