April 20, 2020 at 12:00 am
Comments posted to this topic are about the item A Data Science Approach in Product Analytics
April 20, 2020 at 12:19 pm
Great article, points extra for mentioning Uruguay 🙂
A. Mauricio Repetto
ML Engineer
April 20, 2020 at 12:52 pm
Damn i should get into R
I want to be the very best
Like no one ever was
April 20, 2020 at 3:26 pm
I think that one of the most important parts of all of this can be found in the conclusion section of the article. In particular, I'm talking about the following paragraph....
It is common to see non-significant test results after conducting an A/B test. What constitutes significance comes from product teams who take the 'cost of making a change' into consideration. Since a lot of the times this cost can be large, even if the test is barely a success, the product team may decide to go against making that change.
It definitely IS common to seen "non-significant test results" and that's actually a HUGE problem because of all the time already spent on developing a change and then going through A/B and a whole bunch of other testing.
What is that HUGE problem? It can be any one of any combination of the following...
--Jeff Moden
Change is inevitable... Change for the better is not.
April 21, 2020 at 11:14 pm
Glad you liked it! Thank you!
April 21, 2020 at 11:16 pm
Great points there Jeff! Thanks for reading.
May 24, 2022 at 4:50 am
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