Hypothesis Testing
Hypothesis testing is a statistical method used to make inferences about a population based on sample data. It involves formulating a null hypothesis (a default assumption) and an alternative hypothesis, then using statistical tests to determine whether to reject the null hypothesis in favor of the alternative. This process helps in decision-making, such as validating experimental results, assessing model performance, or detecting significant differences in data.
Developers should learn hypothesis testing when working with data-driven applications, A/B testing, machine learning model evaluation, or any scenario requiring statistical validation. It is essential for ensuring that observed effects are not due to random chance, such as in user behavior analysis, algorithm comparisons, or quality assurance testing. Mastery of hypothesis testing enables developers to make evidence-based decisions and communicate results with statistical rigor.