Which of the following best describes a "hypothesis" in the context of A/B testing?

A hypothesis in the context of A/B testing is fundamentally an assumption about what changes might enhance performance. It serves as the starting point for testing, indicating a specific change that you believe could lead to better results, whether that be higher open rates, click-through rates, or conversions. By formulating a hypothesis, marketers can establish a clear direction for their testing efforts, allowing them to measure the impact of specific variations against a control.

For instance, if a marketer hypothesizes that changing the subject line of an email will improve the open rate, they can set up an A/B test to compare the performance of the original subject line against the new one. This methodical approach provides a framework for drawing data-driven conclusions.

While the other options touch on various aspects of email marketing, they do not encapsulate the essence of what a hypothesis entails in A/B testing, which is specifically about making a conjecture that can be tested and validated through experimentation. A goal statement, a list of variables, or predicting sales outcomes does not reflect the explanatory nature of a hypothesis aimed at exploring potential improvements through specified changes.