thank you so much for this!!! I just was NOT understanding confounding variables but you made it so easy so thank you sincerely from the bottom of my heart! -a psychology student
I think a confounding variable is an extraneous variable (non-treatment) variable which we are not testing in our experiment / study but it (the confounding / extraneous variable) has an effect on the response variable. I will be glad if I'm corrected but that's how I understand this concept. Thank you from Uganda East Africa
Firstly. Thank you Liz for this, you saved my Life. Put playback speed at 1.5x if you are native speaker. Put playback speed at 1.25x if English is second language. Thank me later
I am wondering does the present of confounding always mean a spurious association between risk factor and outcome? Is it possible that confounding can also mask the association between them?
The arrows are correct. in this example she was saying that’s it’s a negative (inverse) correlation, meaning that the younger you are the less likely you’re getting MI, and the more you engage in physical activity the less likely you’re of getting MI
Association does not imply causation! This is what my statistic book has written down on every page. How come you are throwing this causes this and that causes this all over the place? :)
These examples are so cut and clear that your argument is basically invalid. But yes sometimes it can be difficult to deem something an association or causation.
Your example with age is throwing me off. Usually age is an effect modifier. Is it because you portrayed age as a dichotemous variable i.e young or not young that it works? Age and physical exercise would be a continuum spectrum where physical activity would drop gradually as age increases, therefore this is a bad example since there is no singular point where you suddenly shift from being young to not being young anymore. Age is almost always an effect modifier in my opinion, as effect modifiers are usually biologically rooted.
