Difference between A/B Testing and Hypothesis Testing

Software testing plays a critical role in delivering quality applications to the market. Without testing software, no development firm would market it. According to the PRNewswire report, the software testing market will more likely to grow by 34.49 bn USD during 2021-2025. It will accelerate its market growth at a CAGR of 12.39 percent. In this article, we will gather insight into the differences between A/B testing and hypothesis testing.

What is software testing?

Software testing is a branch of software development that determines the quality of the software. It helps in assessing the artifact & behavior of the software to check whether it is free from bugs & flaws. The quality assurance team checks the application against different scenarios, environments, and hardware to render the best quality software in the market. Apart from preventing bugs, software testing is also responsible for minimizing development costs and enhancing performance. Some common types of software testing are:

• Acceptance testing

• Integration testing

• Unit testing

• Regression testing

• Performance testing

• Usability testing

• Black box testing

• White-box testing

• A/B Testing

• Hypothesis testing

What is A/B Testing?[Definition]

A/B testing is a type of software testing where two or multiple versions of the prototype get tested against each other to check. It checks which version appeals more to visitors (in the case of the user interface) or which one performs better (in the case of programming logic). The quality assurance team runs the test on web applications, smartphone apps, ads, and other apps used in hand-held devices to test possible advancements in comparison to a control, performance, logic, etc., compared to the original version. Such type of testing is also called split testing or bucket testing.

What is Hypothesis testing? [Definition]

A hypothesis is a type of software testing and analysis from where the quality assurance team makes inferences of the application based on some data. The statistical inference uses data from a test application to conclude the population probability distribution. Such a test concludes a hypothesis by estimating and analyzing a random sample that gets analyzed - hence the name. In this testing mechanism, the quality assurance team will put a random population sample usually to check two distinct hypotheses: the null hypothesis and the alternative hypothesis.

Difference between A/B Testing and Hypothesis Testing

Conclusion

We hope this article has given you a crisp idea of the differences between A/B testing and hypothesis testing. Software testing plays a significant role during the software development process. The lead testers or the quality assurance team managers should decide which testing technique to opt for based on the requirements and development traits.