What Is Above the Fold and Why Is It Important? (+10 Compelling Examples)
Posted: Tue Dec 24, 2024 8:08 am
3 A/B testing mistakes to avoid
The last thing you want is to dedicate all that effort and marketing budget into split testing, only to get a false positive or an inaccurate test result. Here’s how to avoid the most common (and costly!) mistakes:
Mistake 1: Changing more than one element
When conducting an A/B test, you should only change one element at a time so that you can accurately determine the impact of that specific change.
Are you testing the effect of changing a button color? Then change only the color of the button in the challenger variant and nothing else. If you also change the text on the russia email list button or the layout of the page, you’ll find it difficult to determine which change had the greatest impact on the results.
Changing multiple elements at once can also lead to inaccurate results as the changes may interact with one another in unexpected ways.
Mistake 2: Ignoring the statistical significance
In A/B testing, it’s possible that the results of a test come from chance rather than a true difference in the effectiveness of the variants. This can lead to false conclusions about which variant is better, resulting in poor decisions based on inaccurate data.
Here’s an example: your test shows that variation A has a slightly higher conversion rate than variation B, but you don’t take into account how significant the results are. So you end up concluding that variation A is the better option. However, considering the statistical significance would have made it clear there wasn’t enough evidence to conclude that variant A was indeed better.
The last thing you want is to dedicate all that effort and marketing budget into split testing, only to get a false positive or an inaccurate test result. Here’s how to avoid the most common (and costly!) mistakes:
Mistake 1: Changing more than one element
When conducting an A/B test, you should only change one element at a time so that you can accurately determine the impact of that specific change.
Are you testing the effect of changing a button color? Then change only the color of the button in the challenger variant and nothing else. If you also change the text on the russia email list button or the layout of the page, you’ll find it difficult to determine which change had the greatest impact on the results.
Changing multiple elements at once can also lead to inaccurate results as the changes may interact with one another in unexpected ways.
Mistake 2: Ignoring the statistical significance
In A/B testing, it’s possible that the results of a test come from chance rather than a true difference in the effectiveness of the variants. This can lead to false conclusions about which variant is better, resulting in poor decisions based on inaccurate data.
Here’s an example: your test shows that variation A has a slightly higher conversion rate than variation B, but you don’t take into account how significant the results are. So you end up concluding that variation A is the better option. However, considering the statistical significance would have made it clear there wasn’t enough evidence to conclude that variant A was indeed better.