For all those who cast aspersions on Social Science’s ability to produce valid replicable findings, they need look no further than pollsters (election polling) in US, who have near perfected the ability to produce accurate results, except of course Rasmussen, which has perfected the art of producing reliably Republican findings.
But how are pollsters able to do that with samples of 1000, samples which if collected naively, vary in the estimates of truth enough to render the estimates pointless? The short answer is that they utilize knowledge about how people behave, and how they are distributed in the population, and adjust the results based on that knowledge. Given their ability to do so with great accuracy, it is likely that pollsters know more about how people vote, why they vote, etc. than political scientists. It is also likely however that social science will remain somewhat unaware of the results as most of the knowledge will be proprietary.
Using these techniques in ‘hypothesis testing’
The traditional (frequentist) ‘model’ of hypothesis testing has shied away from utilizing knowledge about the population. Typically, multiple parameters are estimated simultaneously, using ‘regression’ or any of its sibling methods. Bayesian of course embrace the concept of prior knowledge, though typically shy away from utilizing it fully. One modest and somewhat defensible way to test theories would be to ‘fix’ relation of variables with others, using prior knowledge. So in the domain of voting, one can get away from vagaries of sampling, and directly ‘fix’ black respondents from voting 90% for Democrats with modest decreasing propensity given income. This shrinkage of variance from modeling part of data, or theory, would allow for other parameters from being estimated more reliably.