···
On Thu, Nov 9, 2017 at 8:33 AM, Greg Sutcliffe <greg@emeraldreverie.org> wrote:On 08/11/17 17:01, Ivan Necas wrote:
And, if we meet in some time (let's say 6 months from the kick off)
and look at the numbers, I think it would be much easier discussion
if we should ditch the list or not. Additionally, we would have 6
months of hands on experience with Discourse.
I'm going to assume we're talking about side-by-side for a single
channel (i.e the users list). I'll focus on that, but I remain open to
the option of running -dev on a list and -users on a forum, we can make
that work. Still not my top choice, but doable.
So, I'm all for following the data - in fact I'm actually studying Data
Science at the moment in my spare time so I can be better at this for
our community metrics in general (this course [1], good fun).
What I think you're proposing is that we measure the amount of activity
on both platforms after 6 months, and then use that as an indicator of
how much people *like* each platform. The problem is that this
experiment contains both systemic bais and a confounding variable.
Firstly, the systemic bias: people will stay where the conversation is
(i.e. the network effect). Unless everyone moves, no-one does. If we had
zero users on both platforms at the start, this could probably be
accounted for, but that's not the case.
Secondly, the counfounding variable (nemesis of all data scientists).
You're suggesting that "amount of activity" on a given platform (X) can
be used to infer "willingness to use" that platform (Y). But this
doesn't account for the procrastination problem (there are studies, I
picked a couple [2,3]). People don't change if they don't *have* to,
however much better the alternative is. So there's a variable affecting
X but not Y that we can't account for, which means we can't use it for
inference.
I'm not suggesting 100% agreement, on the other hand I'm serious
about listening carefully to the people that actually ARE active in
the community.
100% agree with that, that's exactly what this thread is for. I'll post
that summary I mentioned shortly to try and loop some more voices in.
Cheers,
Greg
[1] https://www.coursera.org/specializations/jhu-data-science
[2] Opt-in vs opt-out organ donation - much higher donation rate with
opt-out. People could *save lives* by filling out a form, and they don't.
[3] Electricity costs. 14 million houses in the UK could be saving £200
per year (2016 data), but they don't switch. UK is actually
considerating putting automatic tariff switching into law because people
are so bad at this.
https://www.gov.uk/government/publications/household-energy-
savings-through-switching-supporting-evidence/many-
households-could-save-around-200-per-year-through-
switching-energy-supplier-basis-for-claim
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