There are so many complications because of data fraud and the fear of perception of data fraud.
I'm the guy who builds the experiments on a team of user researchers. There are all sorts of things that seem intuitive to an outsider but are poo-pooed by practitioners as unethical. For instance, you might run a study that doesn't have enough participants to have a statistically significant conclusion. An outsider would deploy it to more participants to see if the trend becomes significant with more data. A trained researcher will cringe at that proposal.
So far as I can tell, researchers consider the experiment final as soon as you peek at the data. If you want any changes - more data, different demographics, etc - you have to throw out everything and start over. Even though it's logically interchangeable, the data you've already collected is considered spoiled, because they don't want allegations of tampering/data grooming.
I'm the guy who builds the experiments on a team of user researchers. There are all sorts of things that seem intuitive to an outsider but are poo-pooed by practitioners as unethical. For instance, you might run a study that doesn't have enough participants to have a statistically significant conclusion. An outsider would deploy it to more participants to see if the trend becomes significant with more data. A trained researcher will cringe at that proposal.
So far as I can tell, researchers consider the experiment final as soon as you peek at the data. If you want any changes - more data, different demographics, etc - you have to throw out everything and start over. Even though it's logically interchangeable, the data you've already collected is considered spoiled, because they don't want allegations of tampering/data grooming.