Parametric Tests
Parametric tests are statistical hypothesis tests that assume the data follows a specific probability distribution, typically a normal distribution, and rely on parameters like mean and variance. They are used to make inferences about population parameters based on sample data, such as comparing means between groups or assessing relationships. Common examples include t-tests, ANOVA, and Pearson correlation.
Developers should learn parametric tests when working with data analysis, machine learning, or A/B testing in software development, as they provide powerful and efficient methods for hypothesis testing under distributional assumptions. They are particularly useful for analyzing continuous data from controlled experiments, such as comparing performance metrics between different algorithm implementations or user engagement across app versions.