But if you apply statistics, you will see that the open rates only differ significantly 5% of the time, if you use a 95% confidence interval.
If you end the test the moment the data reaches 95% significance, it will show a difference about 50% of the time for the same email. Many people make this mistake.
A 95% confidence interval doesn't mean much if you dont follow good statistical practices.
A small sample size would lower your power to detect meaningful differences, which the original scenario doesn't have (by definition).
(If distributional assumptions, etc, are violated, then that's a different story!)
https://heapanalytics.com/blog/data-stories/dont-stop-your-a...
The point I was trying to make was you can decide to run a test with a very small sample (e.g. n = 5), and it will still have the level of type 1 power you set if you chose a significance level of .05.
> You need to make sure you have enough samples in order to know if you rejected the null hypothesis by chance.
You do this when you decide the significance level (e.g. .05). The value needed to reject, given a significance level, is a function of sample size.
The definition of Type 1 error on wikipedia has a good explanation of this:
What you're saying is perhaps correct but it's not how AB testing is done in the real world. I think many tests show a difference for random reasons.
b) That's assuming you are using statistics right. It's quite hard to use statistics right.
Did you know, in experimentation programs run by Microsoft, Google and Amazon, roughly two thirds of their ideas have no impact or hurt the metrics they were designed to improve? And yet rookie web Devs or marketing assistants "know" better.
Source: https://www.google.com.au/url?sa=t&source=web&rct=j&url=http... (Read section three of this paper by a Microsoft distinguished engineer)
I'll point out a major difference, though: Microsoft, Google, and Amazon are already highly optimized. They've had millions of man-hours put into optimizing everything from product design to UI to UI element positioning to wording to colors to fonts. When you get a new, beneficial result in a change to Google, it's usually because the environment has changed while you weren't looking, and the user is different.
That doesn't apply to a new business that's just learning how to sell their product. In a new business, by definition, you've spent zero time micro-optimizing your message & product. You can get really big wins almost by accident, if you happen to have stumbled into a product category where users actually want what you're building.