![]() In this example, it would also be important to evaluate the timing of the measured variables - does an increase in the amount of hand washing precede a decrease in colds or did it happen at the same time? However, it is also possible that children who have colds are made to wash their hands more often. For example, you might suspect that the number of times children wash their hands might be causally related to the number of cases of the common cold amongst the children at a pre-school. Check for the possibility that the response might be directly affecting the explanatory variable (rather than the other way around).Thus, it is crucial to evaluate and eliminate the key alternative (non-causal) relationships outlined in section 6.2 to build evidence toward causation. ![]() However, outside of randomized experiments, there are numerous other possible reasons that might underlie the correlation. It is often tempting to suggest that, when the correlation is statistically significant, the change in one variable causes the change in the other variable. ![]()
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