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Wouldn't bad A/B testing be a 50/50 solution. Is making decisions on data without statistical significance any better than throwing darts blind?

Consider the bad A/B testing algorithm implemented by Dr. Awesome. Instead of reporting statistical significance, if it would be statistically significant below 10% chance of coincidence, he reliably reports "It was Awesome!" Dr. Awesome then promptly burns his notes to warm his awesome heart.

Even though many people would take issue with using 10% (a lot of practitioners like 5%) and Dr. Awesome has some serious issues with data retention policies, if you always follow Dr. Awesome's advice, you'll win 9 times for every time you lose. I'll take those odds.

Say Dr. Awesome also has one additional problem: one time of twenty, regardless of the results of the A/B test, he just can't help himself and says "It was Awesome!" anyhow. If you follow his advice, you'll now win approximately 5 times for every time you lose. I'll take those odds, too.

There is a large gap between true statistical significance and a fair coin toss. Bad testing (of whatever flavor A/B, MAB, etc.) is likely to land somewhere in that gap. Most likely worse off than proper testing but also quite likely better than tossing the coin or throwing darts.

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