> The idea is to remove the social or political problems from the process.

Everyone in basically any context wants to remove social and political problems. Ignoring them is not the same as removing them.

> Define the goals and feature sets (this is the part of the process that
> requires community interaction), implement and test the changes, review the
> results. The data is the voice of the community. It's what proves if an idea
> is good or bad.
>
> As I said before, though, there's always some vocal minority that will hate

To be clear I dont think every small vocal minority needs to be taken into account and i dont think wikipedians do either. Sometimes people seem to use the word vocal minority for a majority of users in some class.

> change, even when it's presented with data proving it to be good. These
> people should be ignored at this stage of the process. They can continue to
> provide input to future changes, but the data should be authoritative.

Data does not prove things "good". Data proves (or more likely provides some support but not proves) some objective hypothesis. Proving normative claims with objective data is pretty impossible.

That may sound pendantic, but i think its an important distinction. Evidence should be presented in the form of "This change improved findability of the edit button by 40% among anons in our experiment [link to details]. Therefor I/we believe this is a good change because I/we think that findability of edit button is important". Separating what the data proves and what are personal opinions about the data is important to make the "science" sound legitament and not manipulatrd.

>

>
> There's not really a lack of principles, there's a lack of reasonable
> process. What's wrong with change guided by data science? We know the
> scientific process work.

We know its also extremely easy to manipulate, especially when the science is only done by one party that has a specific objective. It can also be myopic, concentrating on one factor well ignoring the holistic whole.

Ultimately the usefulness depends on the skill of whomever is doesigning and conducting the experiments.

> The current process is design by a committee
> that's comprised mostly of people untrained in the field, with no data
> proving anyone's case. Even when there is data it's often ignored in favor
> of consensus of the editor community.

Consensus of the editor commmunity is ancedotal data. That data may be extremely biased and should be evaluated carefully. But it doesnt make sense to just throw it out totally, particularaly in cases where its the only data we have. We should also be evaluating why consensus and data are conflicting. Maybe there are unstudied factors causing the conflict so the two positions are not mutually exclusive.

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