Statistically speaking
As we’ve already suggested, quality and quantity of data matters with A/B testing. This is to minimise the chance of a random result being seen as gospel when in fact it is a false prophet.
To ensure the quality of your results, there are a number of test parameters that can be deployed to guarantee what you’re proposing as a result of the testing is based on verified insights rather than a “gut feel” backed by skewed data. Even partially inaccurate results can steer, for example, a new product introduction wildly off course.
That’s why the concept of statistical significance, which helps quantify whether a result is likely due to chance or to some factor of interest, is crucial when designing an A/B testing programme that is sufficient for your needs. It makes certain that “randomness” doesn’t creep into your results. Moreover, you don’t have to be a prize-winning mathematician to get it right (although it could help) but, better yet, you can use Napier’s A/B test calculator for free via our website. Our math geniuses have already done the hard work, so you don’t have to.