What is the test about?
The A/B Testing test evaluates a candidate's knowledge and understanding of A/B testing concepts and best practices. It encompasses the understanding of experimental design, hypothesis testing, and statistical significance.
Test creator
Lidia Krasowski
Head of CSM for Enterprise Customers at Sellics
Highly skilled in enhancing customer experience and amplifying client value, Lidia Krasowski brings over 7 years of professional experience in Customer Success Management, primarily in the SAAS and Online Marketing fields. Lidia's proficiency lies in fostering enduring business relationships, strategizing successful Amazon advertising campaigns, optimizing content, and managing reviews. With roles at renowned companies like Sellics and Marin Software, she has demonstrated exceptional ability in client account management, team mentorship, and educational webinars. Her tenure at Arvato Financial Solutions further solidified her capabilities in advertising campaign optimization. Lidia holds a Bachelor's degree in International Business Studies from Universität Paderborn and a Master's in International Business and Management from Ichec Brussels Management School, reinforcing her expert status in her field.
Who should take this test?
Data Scientist, Statistician, Web Application Tester
Description
A/B Testing, also known as split testing, refers to a method of comparing two versions of a webpage or other user experience to determine which one performs better. A/B testing is essentially an experiment where two or more variants of a page are shown to users at random, and statistical analysis is used to determine which variation performs better for a given conversion goal.
This A/B Testing assessment examines the candidate's ability to design, implement, analyze, and interpret A/B tests. It tests their knowledge in development of hypotheses, setting up experiments, analyzing results, and making data-driven decisions. It also encompasses the understanding of statistical analysis to ascertain results' validity.
A successful candidate will demonstrate a strong proficiency in understanding and applying A/B testing principles, methodologies, and tools. They will have the ability to critically analyze and interpret results from A/B testing to make data-driven decisions that will enhance user experience and achieve business goals.