HypotTests

HypotTests is a term used to describe a framework of hypothesis testing procedures designed to assess predefined propositions about data, experiments, models, or observed phenomena. Rather than referring to a single statistical test, HypotTests denotes a family of methods that share a common goal: to determine whether evidence in the data supports a specified null hypothesis or favors an alternative hypothesis.

Each HypotTest frame follows the standard elements of hypothesis testing: a null hypothesis H0, an alternative H1, a test statistic calculated from the data, a sampling distribution assumed under H0, and a decision rule based on a predefined significance level alpha. Key performance measures include p-values, power, and effect size. Depending on data type and assumptions, HypotTests can employ parametric tests, nonparametric tests, or resampling approaches such as permutation tests and bootstrap-based procedures.

Applications of HypotTests span many fields, including statistics, psychology, clinical research, bioinformatics, and machine learning. They are used to compare means or proportions, assess independence between variables, or evaluate whether a model’s performance exceeds a baseline. In practice, analysts select an appropriate test within the HypotTests framework based on data distribution, sample size, and the research question.

Limitations of HypotTests include sensitivity to model assumptions, potential for misinterpretation of p-values, and issues arising from multiple testing or dependent data. Emphasizing effect sizes, confidence intervals, and pre-registered analysis plans helps mitigate these concerns.

Related concepts include hypothesis testing, statistical significance, p-values, power analysis, multiple testing correction, and permutation or bootstrap methods.