A/B testing is utilized in marketing as a testing method, the results of which can be analyzed to further develop operations. Testing is an essential part of lean marketing, where emphasis is placed on what works best and ineffective methods are eliminated.
With the advancement of digital tools, A/B testing has become both more common and more streamlined but has also become more complex as automated campaigns have become more common Automated A/B tests are also available on many platforms.
To ensure the reliability of A/B test results, it’s important to check for statistical significance. Statistical significance refers to the probability that the same result would be obtained if the test were repeated. Ignoring statistical significance can lead to erroneous conclusions and further development based on incorrect information.
Key principles of A/B testing
When conducting A/B testing, certain principles must be considered:
Simultaneity: Test elements should be tested simultaneously. Testing versions at different times may introduce temporal biases, such as changes in user behavior. Additionally, it’s advisable to choose a testing period that aligns with normal business operations.
Single Variable: Only one variable should be tested at a time. Testing multiple variables simultaneously makes it difficult to determine which variables influenced the outcome, such as in advertising.
Sample Size: The sample size should be sufficiently large, as larger samples reduce the likelihood of random errors. Drawing conclusions from a survey of 10 people versus 1000 people, for example, yields vastly different levels of reliability. Therefore, sufficiently large target groups are crucial.
Other Considerations: During testing, the budget should be evenly distributed between the test versions to ensure they are in similar situations. The testing period should also be sufficiently long to accumulate data, which requires a sizable budget. The variables being tested should be materially different and significant for business objectives.
Statistical significance
Statistical significance ensures that test results are not merely due to chance but are closer to the truth. While results are never 100% certain, they can be very close to the truth. Typically, the risk level for an incorrect conclusion is set at 10%, 5%, 1%, or 0.1%. This means that results can be deemed accurate with 90.1%-99.9% certainty. Each company must determine its own risk level when conducting statistical testing.
When measuring statistical significance, a hypothesis is first established and then tested for accuracy. The null hypothesis assumes no difference between the variables being tested. Based on our hypothesis, we assume that there is a difference between the variables being tested, at least according to a certain metric. It’s important to note that sometimes statistical significance is not achieved, indicating that no clear insights were gained from the test, and it’s time to move forward.
After testing
Following testing, it may appear at first glance that there is a difference between the variables being tested. However, the difference may be so small that it is not statistically significant, and conclusions should not be drawn based on it. It’s advisable to examine statistical significance using a dedicated tool, such as AB Testguide.
How does your company ensure the accuracy of its results? Please reach out if you’d like to discuss your approach to measuring statistical significance or learn more about our processes!
Also read:
A/B Testing in Marketing Using the Lean Loop
Ideas for A/B testing in advertising