Editing Talk:882: Significant

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One thing I've gleaned from this is that they apparently opened a bag of Jelly Bellys or Gimball's and tested them in whatever order. I say this because they hit colors you'd never see in the smaller-palette brands of jelly beans (brown, teal, salmon) before some very common colors (red, yellow, black, green). If it were me, I would probably have started with a smaller-palette brand, since their colors affect ''everyone'' who eats jelly beans, and not just the ones who go for the gourmet brands. [[User:Nyperold|Nyperold]] ([[User talk:Nyperold|talk]]) 12:58, 6 July 2017 (UTC)
 
One thing I've gleaned from this is that they apparently opened a bag of Jelly Bellys or Gimball's and tested them in whatever order. I say this because they hit colors you'd never see in the smaller-palette brands of jelly beans (brown, teal, salmon) before some very common colors (red, yellow, black, green). If it were me, I would probably have started with a smaller-palette brand, since their colors affect ''everyone'' who eats jelly beans, and not just the ones who go for the gourmet brands. [[User:Nyperold|Nyperold]] ([[User talk:Nyperold|talk]]) 12:58, 6 July 2017 (UTC)
βˆ’
 
 
This kind of error is why you use ANOVA.
 
This kind of error is why you use ANOVA.
 
[[Special:Contributions/162.158.63.238|162.158.63.238]] 20:21, 29 October 2018 (UTC)
 
[[Special:Contributions/162.158.63.238|162.158.63.238]] 20:21, 29 October 2018 (UTC)
  
 
Want to reiterate that using "95% confidence" for statistical significance means having a threshold of <.05 for the p-value, and the p-value is the probability noise alone would have generated a change this big or bigger.  If all you ran all day long were a/a tests (randomly assign people to two groups but give them the exact same experience) then 5% of your tests would be stat sig for any given metric.  However, the chance of at least one false positive over 20 tests is only 64% (1-.95^20), not 100%.  But of course, you also might get MORE than 1 false positive in 20 experiments, so the expected value for the NUMBER of false positives after 20 experiments IS 1.  If that hurt your brain, welcome to probability!
 
Want to reiterate that using "95% confidence" for statistical significance means having a threshold of <.05 for the p-value, and the p-value is the probability noise alone would have generated a change this big or bigger.  If all you ran all day long were a/a tests (randomly assign people to two groups but give them the exact same experience) then 5% of your tests would be stat sig for any given metric.  However, the chance of at least one false positive over 20 tests is only 64% (1-.95^20), not 100%.  But of course, you also might get MORE than 1 false positive in 20 experiments, so the expected value for the NUMBER of false positives after 20 experiments IS 1.  If that hurt your brain, welcome to probability!

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