技能档案

Hypothesis Testing in ML

此技能定义了各角色和级别的期望。

Machine Learning & AI Classical Machine Learning

角色数

包含此技能的角色

级别数

结构化成长路径

必要要求

其余 5 个可选

Machine Learning & AI

Classical Machine Learning

2026/2/22

选择当前级别并对比期望。下方卡片显示晋升所需掌握的内容。

各级别期望

表格展示从初级到首席的技能深度变化。点击行查看详情。

角色	必要性	描述
Data Scientist		Understands the fundamentals of Hypothesis Testing in ML. Applies basic practices in daily work. Follows recommendations from the team and documentation.

角色	必要性	描述
Data Scientist		Applies specialized statistical tests for ML tasks: bootstrap hypothesis testing, permutation tests, multiple comparison correction (Bonferroni, FDR). Calculates confidence intervals for ML metrics. Conducts statistical significance testing of models.

角色	必要性	描述
Data Scientist		Designs frameworks for statistical validation of ML models. Applies sequential testing, always-valid confidence intervals for continuous monitoring. Uses causal inference: propensity score matching, regression discontinuity for ML impact assessment.

角色	必要性	描述
Data Scientist		Defines statistical validation standards for the data science team. Establishes guidelines for test selection, sample size calculation, and result interpretation. Coordinates statistical infrastructure development for ML experiments.

角色	必要性	描述
Data Scientist		Shapes statistical literacy culture in the organization. Defines evidence-based decision-making standards. Publishes methodological works on statistical testing in ML contexts, trains the organization.

Junior 1 要求

Data Scientist

Understands the fundamentals of Hypothesis Testing in ML. Applies basic practices in daily work. Follows recommendations from the team and documentation.

Middle 1 要求

Data Scientist

Applies specialized statistical tests for ML tasks: bootstrap hypothesis testing, permutation tests, multiple comparison correction (Bonferroni, FDR). Calculates confidence intervals for ML metrics. Conducts statistical significance testing of models.

Senior 1 要求

Data Scientist

Designs frameworks for statistical validation of ML models. Applies sequential testing, always-valid confidence intervals for continuous monitoring. Uses causal inference: propensity score matching, regression discontinuity for ML impact assessment.

Lead / Staff 1 要求

Data Scientist

Defines statistical validation standards for the data science team. Establishes guidelines for test selection, sample size calculation, and result interpretation. Coordinates statistical infrastructure development for ML experiments.

Principal 1 要求

Data Scientist

Shapes statistical literacy culture in the organization. Defines evidence-based decision-making standards. Publishes methodological works on statistical testing in ML contexts, trains the organization.

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