There’s a 5% chance that these results are total bullshit
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There’s a 5% chance that these results are total bullshit
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Re: There’s a 5% chance that these results are total bullshit
#2I've long argued that the biggest problem with orthodox NHST for A/B testing is that you actually don't care about 'significance of effect' as much as you do 'magnitude of effect'. Furthermore, p-values tell you nothing about the range of possible improvements (or lack thereof) you're facing. Maybe you are willing to risk potential losses for potentially huge gains, or maybe you can't afford to lose a single customer and would rather exchange time for certainty.
My favored approach I've outlined here[0]. Where the problem is basically considered one of Bayesian parameter estimation. Benefits include:
1. Output is a range of possible improvements so you can reason about risk/reward for calling a test early.
2. Allows the use of prior information to prevent very early stopping, and provide better estimates early on.
3. Every piece of the testing setup is, imho, easy to understand (ignore this benefit if you can comfortably derive Student's T-distribution from first principles)
[0] https://www.countbayesie.com/blog/2015/4/25/bayesian-ab-test...
Re: There’s a 5% chance that these results are total bullshit
#3I'm not a statistician, but lately I've been wondering:
When we're A/B testing code, the code is already written. If there's a 5%, or even 15% chance of it being bullshit, who cares? The effort is usually exactly the same if I switch or not.
It's my understanding that 95%, 99%, etc, were established for things that require extra change. We don't want to spend extra time developing and marketing a new drug if it isn't effective. We don't want to tell people to do A instead of B if we aren't sure A is really better than B.
But in software I've already spent all the time I need to to implement the variation on the feature. So given that, why do I need 95%?
I would appreciate if someone with more knowledge can answer this question.
Edit to add: I see a lot of answers about the cost to keep the code around. What about A/B tests that don't require extra code, just different code? Most of our A/B tests fall into this category.
Re: There’s a 5% chance that these results are total bullshit
#4[deleted]
Re: There’s a 5% chance that these results are total bullshit
#5I'm not a statistician, but lately I've been wondering: When we're A/B testing code, the code is already written. If there's a 5%, or even 15% chance of it being bullshit, who cares? The effort is usually exactly the same if I switch or not. It's my understanding that 95%, 99%, etc, were established for things that require extra change. We don't want to spend extra time developing and marketing a new drug if it isn't…
Would you push a feature that negative affected your product? 95% confidence you will be able to know if you're feature is indeed positive, negative, or roughly neutral.
Re: There’s a 5% chance that these results are total bullshit
#6> Stop saying: “We’ve reached 95% statistical significance.”
> And start saying: “There’s a 5% chance that these results are total bullshit.”
Argh, no, no, no and no!
95% significance is NOT 95% probability! When you select a confidence level of a 95%, the probability that your results are nonsense is ZERO or ONE. There is no probability statement associated to it. Just because something is unknown does not mean that you can make a probability statement about it, and the mathematics around statistical testing all depend on the assumption that the parameter being tested is not random, merely unknown...
Rather, 95% statistical significance means, we got this number from a procedure that 95% of the time produces the right thing, but we have no idea whether this particular number we got is correct or not.
UNLESS!
Unless you're doing Bayesian stats. But in that case your procedure looks completely different and produces very different probability intervals instead of confidence intervals, and you don't talk about statistical significance at all, but about raw probabilities.
Re: There’s a 5% chance that these results are total bullshit
#7No.
In Frequentist thinking; p=0.05 means that if there was in reality no difference in your A and B and you repeated the experiment many times, 5% of the observed differences would be equal to or greater than the difference you just measured.
No probabilistic statement about the results being correct or incorrect can be made from a Null-Hypothesis significance test.
Re: There’s a 5% chance that these results are total bullshit
#8It gets even worse.
If you try 100 tests, and pick the 5 that pass the Statistically Significant threshold, most likely all 5 are BS.
Re: There’s a 5% chance that these results are total bullshit
#9I'm not a statistician, but lately I've been wondering: When we're A/B testing code, the code is already written. If there's a 5%, or even 15% chance of it being bullshit, who cares? The effort is usually exactly the same if I switch or not. It's my understanding that 95%, 99%, etc, were established for things that require extra change. We don't want to spend extra time developing and marketing a new drug if it isn't…
Keeping the code around has a cost: the cost of maintaining, reading, compiling, deploying, and building around it. If the new feature isn't adding value, all it's doing is adding to the cost of real value-features you want to develop later.
Re: There’s a 5% chance that these results are total bullshit
#10I'm not a statistician, but lately I've been wondering: When we're A/B testing code, the code is already written. If there's a 5%, or even 15% chance of it being bullshit, who cares? The effort is usually exactly the same if I switch or not. It's my understanding that 95%, 99%, etc, were established for things that require extra change. We don't want to spend extra time developing and marketing a new drug if it isn't…
Would you push a feature that negative affected your product? 95% confidence you will be able to know if you're feature is indeed positive, negative, or roughly neutral.
I think the core question is:
validation of upside vs validation of downside
as in: i want to avoid pushing something that is worse but i am optimistic (up to even indifferent) about how much something is better
personal opinion: data trains gut-feeling