I was saying that if there was any reason to suspect height might be a factor, then height should be added to the factors considered when trying to make the groups indistinguishable from each other. If height isn’t suspected to be a factor, adding height to those factors with a low weight does almost no harm to the rest of the distribution.
Is there any excuse for the measured variable to notably differ between the control and experimental groups in a well-executed experiment?
I was saying that if there was any reason to suspect height might be a factor, then height should be added to the factors considered when trying to make the groups indistinguishable from each other.
In a perfect world, perhaps. But every variable is more effort, and you need to do it from the start or else you might wind up screwing things up (imagine processing people one by one over a few weeks and starting their intervention, and half-way through, noticing that height is differing between the groups...?)
Is there any excuse for the measured variable to notably differ between the control and experimental groups in a well-executed experiment?
If you didn’t balance them, it may easily happen. And the more variables that describe each person, the more likely the groups will be unbalanced by some variable. People are complex like that. If you’re interested in the topic, I’ve already pointed you at the Wikipedia articles, but you could also check out Ziliak’s papers.
I see where gathering information about all participants before starting the intervention might not be possible. It should still be possible to maximize balance with each batch added, but that means a tradeoff between balancing each batch and balancing the experiment as a whole. For a given experiment, we would have to decide the relative likelihood that that there would be a confounding variable which in the batches or a confounding variable in the demographics.
The undetected confounding variable is always a possibility. That doesn’t mean that we can’t or shouldn’t do as much about it as the expected gains offset the expected costs, and doing some really complicated math to divide the sample into two groups isn’t much more expensive than collecting the data to go into it.
I was saying that if there was any reason to suspect height might be a factor, then height should be added to the factors considered when trying to make the groups indistinguishable from each other. If height isn’t suspected to be a factor, adding height to those factors with a low weight does almost no harm to the rest of the distribution.
Is there any excuse for the measured variable to notably differ between the control and experimental groups in a well-executed experiment?
In a perfect world, perhaps. But every variable is more effort, and you need to do it from the start or else you might wind up screwing things up (imagine processing people one by one over a few weeks and starting their intervention, and half-way through, noticing that height is differing between the groups...?)
If you didn’t balance them, it may easily happen. And the more variables that describe each person, the more likely the groups will be unbalanced by some variable. People are complex like that. If you’re interested in the topic, I’ve already pointed you at the Wikipedia articles, but you could also check out Ziliak’s papers.
I see where gathering information about all participants before starting the intervention might not be possible. It should still be possible to maximize balance with each batch added, but that means a tradeoff between balancing each batch and balancing the experiment as a whole. For a given experiment, we would have to decide the relative likelihood that that there would be a confounding variable which in the batches or a confounding variable in the demographics.
The undetected confounding variable is always a possibility. That doesn’t mean that we can’t or shouldn’t do as much about it as the expected gains offset the expected costs, and doing some really complicated math to divide the sample into two groups isn’t much more expensive than collecting the data to go into it.