One-tailed vs. two-tailed tests

The blog post discusses the significance of choosing between one-tailed and two-tailed hypothesis testing in the context of A/B testing, emphasizing its impact on test planning, data analysis, and result interpretation. A one-tailed test predicts a specific direction of difference between control and treatment groups, which can result in a more efficient test requiring a smaller sample size and is often aligned with specific business objectives. Conversely, a two-tailed test does not specify a direction and can detect differences in either direction, which may require a larger sample size but provides the advantage of identifying both positive and negative effects. The choice between the two should be based on specific business needs, as it influences the required sample size, the power of the test, and the ease of interpreting results, particularly when using confidence intervals. The article highlights that both approaches are valid and the decision should consider factors such as sample size availability and the importance of detecting negative effects.