In A/B testing, the first step is to set a hypothesis. This involves identifying a problem or opportunity in a product or service and making an educated guess about the best solution. The hypothesis outlines the expected outcome of the change that your testing, serving as a big picture goal for the A/B test. For example, if a webpage has a high bounce rate, the hypothesis might be that changing the page design will lower that rate. After the test, the hypothesis proves to be either true or false, making it a crucial concept in the A/B testing process.

Variable selection is the process of identifying and selecting which aspects of a product or service to test. A/B testing only tests one variable at a time to accurately measure its effect. This could be anything from headline text, graphics, calls to action, or page layout. Choosing the right variable is crucial. It should reflect the hypothesis and be something that could realistically impact the metric you're trying to improve. Once the variable is selected, two versions (A and B) are created for the actual test.

In an A/B test, the audience is randomly divided into two groups. Each group sees a different version of the product, either version A or version B. This random division reduces bias and maintains the validity of the test. The goal is to ensure that any difference in behavior between the two groups can be attributed to the variable being tested, rather than some external factor. This also allows for fair comparison between the two versions in the measurement phase.

Measurement is the process of collecting data on how the test groups interact with each version of the product. This data needs to be directly related to the hypothesis defined in the initial stages of the test. The key measurable metrics might include click-through rate, conversion rate, bounce rate, or any other user behavior relevant to the test. The goal of measurement is to determine whether or not the change in the product led to a statistically significant difference in behavior.

Finally, once the data has been collected and analyzed, the results need to be interpreted. This involves analyzing whether the difference in behavior is statistically significant, and if it is, it means that the change had a real impact. If the version B outperformed version A, then the implementation phase involves rolling out the 'winning' version to all users. If version A outperformed B, or if there was no significant difference, then the old version is kept. Either way, the insights from the A/B test can be used to inform future strategies and further testing.

Control groups are an essential part of A/B Testing in Agile methodology. Control groups are selected portions of the user base that are shown the current or 'control' version of a product feature instead of the new 'variant' version. This comparison allows practitioners to measure accurately the effect of the variant. Effective use of control groups ensures that the results aren't skewed by outside influences, as all users continue to experience the same external variables (e.g. seasonal factors, marketing activities).

Statistical significance is a mathematical tool used in A/B testing to conclude whether the difference in conversion rates between two variants is not by chance. This is typically measured with a p-value. If the p-value is below a predetermined threshold (often 0.05), we reject the null hypothesis that suggests that the observed outcome was merely by chance. The lower the p value, the higher is the statistical significance and the more confident we can be in favor of the variant.

While A/B testing tests a variation of one element against the original, multivariate testing allows for testing multiple changes and their interactions simultaneously. This complex analysis could involve different headlines, images, and call-to-actions at the same time to identify the best combination. The main advantage of this testing is obtaining insights into how certain variables work together. It’s important to note that multivariate tests require larger sample sizes to deliver reliable results, due to the multiple combinations being tested.

Validity threats refer to the factors that can jeopardize the accuracy of the A/B test results. They include history effect (external factors influencing users' behavior during the test), selection bias (non-random distribution of users into groups), instrumentation effect (changes in measuring system), and novelty effect (users responding positively to any change because it's new). Awareness and mitigation of these threats improve the quality and reliability of your test results.

Follow-up experiments are often necessary after the initial A/B testing. They aim at testing new hypotheses born from the learnings of the initial test, checking the long-term effects of the changes, or testing the changes on different segments. Follow-up experiments maximize the knowledge that can be gained and ensure that the feature improvements continue fulfilling user needs and business objectives over time.