Post by account_disabled on Feb 22, 2024 5:34:39 GMT -5
The were bound to be similar Why thats exactly what Tyler Vigen did Yes this is possible. Reverse causation Is it possible that we have this the wrong way around For example perhaps your relatives in mourning for your bedsheetrelated death eat cheese in large quantities to comfort themselves This seems pretty unlikely so lets give it a pass. No this is very unlikely. Joint causation Is it possible that some third factor is behind both of these Maybe increasing affluence makes you healthier so you dont die of things like malnutrition and also causes you to eat more cheese This seems very plausible.
Yes this is possible. Linearity Are we comparing two linear trends A linear trend is a America Mobile Number List steady rate of growth or decline. Any two statistics which are both roughly linear over time will be very well correlated. In the graph above both our statistics are trending linearly upwards. If the graph was drawn with different scales they might look completely unrelated like this but because they both have a steady rate theyd still be very well correlated. Yes this looks likely. Broad applicability Is it possible that this relationship only exists in certain niche scenarios or.
At least not in my niche scenario Perhaps for example cheese does this to some people and thats been enough to create this correlation because there are so few bedsheettangling fatalities otherwise Yes this seems possible. So we have Yes answers and one No answer from those checks. If your example doesnt get No answers from those checks its a fail and you dont get to say that the study has established either a ranking factor or a fatal side effect of cheese consumption. A similar process should apply to case studies which are another form of correlation the correlation between you making a change and something good or bad happening. For example ask Have I ruled out other factors.
Yes this is possible. Linearity Are we comparing two linear trends A linear trend is a America Mobile Number List steady rate of growth or decline. Any two statistics which are both roughly linear over time will be very well correlated. In the graph above both our statistics are trending linearly upwards. If the graph was drawn with different scales they might look completely unrelated like this but because they both have a steady rate theyd still be very well correlated. Yes this looks likely. Broad applicability Is it possible that this relationship only exists in certain niche scenarios or.
At least not in my niche scenario Perhaps for example cheese does this to some people and thats been enough to create this correlation because there are so few bedsheettangling fatalities otherwise Yes this seems possible. So we have Yes answers and one No answer from those checks. If your example doesnt get No answers from those checks its a fail and you dont get to say that the study has established either a ranking factor or a fatal side effect of cheese consumption. A similar process should apply to case studies which are another form of correlation the correlation between you making a change and something good or bad happening. For example ask Have I ruled out other factors.