I have a data set where the control group sample size is significantly smaller than the experimental (4-5x in my situation, but I'm interested in the general answer). A colleague was trying to convince me to randomly filter my experimental data down to the same size as my control, but it seems to me that more data is always better.
I should be using all of the data available to me to shrink my error bars as much as possible for means testing, correct? Increasing the variability of one group by artificially decreasing the sample size seems to be counter productive.
Is there any merit to my colleague's suggestion? Does it matter if my control or experimental group is the smaller one?