Correlation and Causality – Don’t Confuse Them (13-2) скачать в хорошем качестве

Correlation and Causality – Don’t Confuse Them (13-2)

If you have been told anything about correlation, it is probably this: correlation does not equal causation. Of course, when one variable causes changes in another variable, they will certainly be correlated; however, just because two things are related does not necessarily mean that one is causing the other. In order to establish that one variable is causing changes in another variable, you have to make sure that there are no other variables that could be causing the change. In an experimental design, the researcher manipulates the X variable (who gets the drug) and measures what happens to the Y variable. This allows the demonstration of causality. In a correlational design, you can’t establish causality because the variables are observed as they occur naturally; no attempt is made to manipulate or control for either one. However, correlational designs allow us to test for things that we never could test in an experimental design. This video teaches the following concepts and techniques: Differences between experimental designs and correlational designs Link to a Google Drive folder with all of the files that I use in the videos including the Effect Size Calculator for t Tests and datasets. As I add new files, they will appear here, as well. https://drive.google.com/drive/folder...