Appsumo reveals its A/B testing secret: only 1 out of 8 tests produce results

http://visualwebsiteoptimizer.com/split-testing-blog/a-b-testing-tips/

by paraschopra • 15 years ago

44 26 15 years ago

patio11• 15 years ago

This is roughly my experience, too -- take a look at how many null results ("no significant difference") I rack up: http://www.bingocardcreator.com/abingo/results

To add insult to injury, half of the remainder were significant... in the wrong direction.

(Edit to add: null results aren't failures, though. What is the Edison quote: you now know one more thing that didn't work.)

paraschopra5• 15 years ago

Yup, same experience with me too (while observing customers' tests and doing my own A/B tests). Noah provided great counter-examples. I mostly publish only positive A/B test results as case studies but this doesn't mean every A/B test produces spectacular results.

tomjen3• 15 years ago

I wouldn't call them failed tests. In science a null result mean that you didn't learn anything but in this case who cares if p is 0.6? It is still more likely to make you money.

barrkel• 15 years ago

I think precisely the point of not being able to reject the null hypothesis is that you can't be sure it's more likely to make you more money; otherwise you could have required a lower confidence level and still rejected the null hypothesis.

bdonlan• 15 years ago

The thing about non-statistically-significant tests is you can't, in fact, say "it is still more likely to make you money." You can say that the B set did in fact make you more money this time, but you can't rule out that being luck of the draw. It might even have a (very small) bias AGAINST making you money, but that bias got lost in the noise. That's why it's important to discard non-statistically-significant tests (or expand the sample size if you _really_ want to know...)

tel• 15 years ago

Testing involves paying a price in example counts in order to buy analytical power at some unknown rate. Insignificant results arise when either (1) people react with a lot of variance and that power/example rate is small or (2) the effect you're trying to analyze is so tiny that you need a great deal of power to see it at all.

Unfortunately, it's difficult to determine the difference between those two cases. If many of your tests are failing to be significant, it could be that you're simply never investing enough to get the power you need: your users are somewhat inflexible to the design changes you're making.

It could also be that many of your changes simply are pretty insignificant and by the time you wait a full year gathering simply infinite numbers of impressions, you'll find it was just a waste of time.

Again, practice statistics with a great deal of self-awareness. They're only meant to inform.

wccrawford• 15 years ago

Every test produces results. Only 1 in 8 produce positive results.

Knowing that 2 different approaches are approximately the same is a great help in design... It eliminates that 'Which way is better?' worry and lets you design it the way that looks best, instead.

paraschopra5• 15 years ago

Yep, it is true that even insignificant results can help you concentrating on how to make it look better. But insignificant results are disappointment if your goal is to increase conversion rate.

ig12• 15 years ago

These aren't failures, if the A/B test shows no impact or that the change is a negative, then both of those add value in telling you what to concentrate on.

Before people did A/B test, it was generally assumed on Facebook ads that the title was the second most important factor on CTR so people spent a lot of time tuning title copy.

After lots of people did A/B testing it turns out that title copy has almost no impact, people switched their title to Chinese (for a non-chinese speaking audience) without seeing any change. That "no significant difference" means that thousands of man hours can be spent elsewhere rather than copywriting ad titles.

paraschopra5• 15 years ago

I'd be wary of concluding too much from other people's A/B tests. Yes, they serve as a good starting point but doing A/B tests yourself is still imperative. Case in point: Noah found out headlines didn't matter to his audience. But for many of our customers, a small change in headline does produce significant results.

ig12• 15 years ago

Sure, I wasn't saying that the headline results were generalizable, but "no significant difference" A/B testing results can tell you to stop wasting time in an area.

noelwelsh4• 15 years ago

A lot of these "no results" are just bad statistics. When a test shows no result it does not mean there is no significant difference. It means that the test cannot reliably tell given the available data. Technically, this is because as you decrease the probability of a type I error (lower the p-value) you increase the probability of a type II error. To lower the probability of a type II error, and hence have null results actually mean there is no significant difference, you needs lots of hits. Under assumptions that are reasonable for most e-commerce sites (e.g. 5% conversion, you want to distinguish changes of 5%) you need about 100'000 hits. Very few people have this much data, and hence their inferences are flawed. You can do a lot better than A/B testing with better maths. More here:

http://untyped.com/untyping/2011/02/11/stop-ab-testing-and-m...

paraschopra5• 15 years ago

I assume people who have run these "no results" A/B tests only gave up after enough traffic to establish no significant result was found given pre-determined X power and Y statistical significance cutoff. We have a calculator here to help determine what is the maximum number of visitors you need before giving up on a test: http://visualwebsiteoptimizer.com/ab-split-test-duration/

noelwelsh4• 15 years ago

That's a pretty big assumption to make. I've seen people doing A/B testing w/ a few thousand hits max, which is not enough for most scenarios.

eljaco• 15 years ago

They might also want to test a link to an FAQ or Details page. I was trying to see if this was only for iPhone or Android but there's really only one option : give us your email.

noahkagan2• 15 years ago

Good point. It's mostly for web or client (Mac / Windows). Might do deals on phone apps in the future. http://appsumo.com/faq

gohat• 15 years ago

I'm not sure these examples are entirely failed tests - rather they teach what is working and what isn't.

noahkagan2• 15 years ago

That's really true. It's funny that some of the changes we weren't expecting to make much of an impact have had the biggest results.

random42• 15 years ago

Negative results are results too.

Appsumo reveals its A/B testing secret: only 1 out of 8 tests produce results

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