Facebook can move the needle with hundreds of 0.1% improvements. A small startup with no revenue and 100 users doesn’t care about 0.1%, nor will they be able to detect it. In a small product with no usage, serial testing is fine: there’s no chance that the business will be built around the existing product, so rapid change is more important than scientific understanding. Statistical significance is the second most important variable in assessing when to A/B test. Making a decision between two options without enough data can undermine the entire point of A/B testing.
Doing statistical significance properly can be difficult, but the 80-20 solution is pretty simple. Just use an online split test calculator, using estimates of statistics to see whether you are likely to attain statistical significance for a single output variable. A big caveat: a little common sense regarding statistical significance can go a long way. If you’ve been running a test for a while and don’t have a clear winner but have some other ideas that might move the needle a lot more, you might be well-served by resolving the test now and trying something new.
注意：統計的有意性に関していくらかの知識をもっていれば非常に役に立つ。しばらくテストを行なってきても、はっきりした正解が得られないが、他に全体に大きな変化を与えられるような考えがあれば、今すぐテストを行なって新しいことをやあってみれば、それが役に立つかもしれない。
You’re running a company, not trying to publish in an academic journal. At Team Rankings, we regularly do this with BracketBrains. Most of our sales happen over a four day period, so we have a limited window in which to test things. The “cost” of resolving slightly sub-optimally — say choosing the 5% option rather than the 5.1% option — is likely to be lower than the opportunity cost of not running an additional test. And since resolving a test when 99% of sales have occurred does us no good, we’re more aggressive than traditional statistical tests would dictate.
If, on the other hand, you’re early in a product’s lifetime, conservative decision-making might be more appropriate. HOW There’s one way to A/B test properly: build your own system. Lots of people probably don’t want to hear that, but products like Optimizely are too simplistic and optimization-focused to be broadly useful. Outsourcing your A/B testing is like outsourcing your relationship with your users: you need to understand how people are using your product, and the A/B testing services currently available don’t cut it. I wish I could recommend an open source A/B testing framework to avoid re-inventing the wheel; ping me if you know of a good one or are creating one (if so, I’d be happy to help).
どのように
適切なA/Bテストを行なうための手段が１つある。それは独自のシステムを築くことだ。おそらく多くの人は聞きたくないだろうが、Optimizelyのような製品は広範囲で役に立つようあまりにも単純化され、効率化に焦点をおかれすぎている。あなたのA/Bテストをアウトソーシングするということはあなたのユーザーとの関係をアウトソーシングするようなものである。あなたは人々がどのようにあなたの製品を利用しているかを理解する必要があり、現在入手可能なA/Bテストサービスはそれを削減することはない。わざわざ初めからまたやり直すということのないよう、オープンソースA/Bテストの体制が推奨できることを願っている。もし何か良いものを知っている、もしくは作成しているのなら私に知らせてほしい（その場合、手助けできたら幸いだ）。
A/Bingo is the closest. The good news is that it’s pretty simple to get your own very basic A/B testing system up and running, and it’s easy to build up functionality over time. Here’s the bare minimum:
Reporting (SQL) I’m assuming you have a USER_ACTIVITY table that records different types of activity, with user, time, and activity type. A table like that makes A/B test reporting a whole lot easier.
Scaling If your site is or becomes massive, scaling the framework will entail some additional work. USER_AB_TEST_OPTIONS may require a large number of writes, and that little query joining USER_ACTIVITY and USER_AB_TEST_OPTIONS might take a while. Writing to the table in batch, using a separate tracking database, and/or using non-SQL options may all help to scale everything. At Circle of Moms, we built out a system to automatically report lots of stats for every test. This was awesome, but it takes some work to scale, and I would never recommend it as a first step. So… As I said, A/B testing is like candy: fun and sometimes addictive.
Done correctly, it can be part of the best form of mature and thoughtful product development. It builds a culture of testing and measuring. It lets you understand what works and what doesn’t. It forces you to get smarter about what actually moves metrics. Most important, it fosters an environment where data trumps opinions… anyone want to volunteer to try to take that to D.C.? Mike Greenfield founded Circle of Moms and Team Rankings, led LinkedIn's analytics team from 2004-2007, and built much of PayPal's early fraud detection technology. Ping him at [first_name] at mikegreenfield.com.
