Bayesian versus Frequentist Statisticians: the war is real

Imagine that you wake up in the one morning and you don’t remember anything from your previous life. You’ve erased all memories from your previous days.

Pretty intense and terrifying, huh?

Well, that’s how Bayesian statisticians describe frequentist colleagues because frequentist statisticians do not use any prior knowledge. Everything you learn about the world is through the lens of events that are happening at the moment and currently.

Michael Hochster explains;

“The essential difference between Bayesian and Frequentist statisticians is in how probability is used.”

As interest grows in conversion optimization and A/B testing, marketers are always searching for a new design that will generate significant uplifts in conversion rates. Because the majority of AB tests fail to produce any meaningful results, many marketers are too eager to declare a winner for a split test.

So, even in the few instances where a testing software declares a winner, there is a good chance that you merely identified a false positive.

How do you avoid that?

You start by having a good handle on A/B testing statistics to ensure that your data is collected and analyzed …

Did you know anywhere from 80% to 90% percent of A/B tests do not produce any significant results?

In fact:

Only 1 out of 8 A/B tests drive significant change.

Done incorrectly, some marketers have begun to question the value of A/B testing…

…their A/B test reports an uplift of 20% and yet, the increase reported by the AB testing software never seems to translate into improvements or profits.

The reason?

“Most winning A/B test results are illusory.” (Source: Qubit)

Furthermore, the majority of arguments that call for running A/A testing consider it a sanity check before you run …

If you missed, we are conducting a Facebook live every Thursday at 12 pm EST.

Last week Ayat spoke about the SHIP method (Scrutinize, Hypothesize, Implement, Propagate). She honed in on the first letter in SHIP, Scrutinize. She summarized what it takes to build out your conversion roadmap so you have a plan for testing and moving forward.

Khalid recently wrote a post on why most case studies suck. Well, it wasn’t actually that case studies that suck, but rather that the AB experiments conducted within these case studies are simply not valid. We also had an interesting discussion about in the Invesp linkedin and the Invesp facebook pages and tweeted about it as well.

The point of this long week debate came down to: process, process, process.

If you follow a sound CRO process and are careful about missteps when it comes to AB testing, you can achieve amazing results and have a successful program.

But for …

The CRO industry have evolved tremendously over the last twelve years.

I still recall conversations we used to have with CMOs and VP of marketing at large online retailers trying to introduce them to conversion optimization.

We would first try to determine if they even knew what were even talking about (it was that new): “Have you considered CRO?”

The first response for the most part was, “CRO? You mean SEO. Yes, we are working on increasing traffic to our website.”

“No, I am not talking about SEO, I am talking CRO…conversion rate optimization. Increasing your website conversion rate.”

You must cover all the bases to get reliable results from your A/B tests.

A/B testing mimics scientific experiments, and similarly will not provide you a 100% certainty in 100% of tests that you run. Only few marketers are aware of the limitations of the method and know how to run it to get valid results and minimize the risk of false positives and false negatives.

That’s why Martin Goodson, now the Chief Scientist at Evolution AI, wrote a paper called “Most Winning A/B Test Results Are Illusionary.” In his paper, he explained that badly performed A/B tests are more …

“Why do I need to learn about statistics in order to run an A/B testing?” You may be inclined to wonder, especially considering that the testing engine supplies you with data to make a judgement on the statistical significance of the test, correct?

As a matter of fact, you have plenty of reasons to learn statistics.

If you’re conducting A/B tests, you need to understand some basics about statistics to validate your tests and their results.

Nobody wants to spend time, money and effort on something that will turn out useless at the end. To use A/B testing efficiently and effectively, …