+   [Machine Learning Mastery 统计学教程](README.md)
+   [浅谈计算正态汇总统计量](a-gentle-introduction-to-calculating-normal-summary-statistics.md)
+   [非参数统计的温和介绍](a-gentle-introduction-to-nonparametric-statistics.md)
+   [Python中正态测试的温和介绍](a-gentle-introduction-to-normality-tests-in-python.md)
+   [浅谈自举法](a-gentle-introduction-to-the-bootstrap-method.md)
+   [浅谈机器学习的中心极限定理](a-gentle-introduction-to-the-central-limit-theorem-for-machine-learning.md)
+   [浅谈机器学习中的大数定律](a-gentle-introduction-to-the-law-of-large-numbers-in-machine-learning.md)
+   [机器学习中的所有统计量](all-of-statistics-for-machine-learning.md)
+   [如何在Python中计算机器学习结果的自举置信区间](calculate-bootstrap-confidence-intervals-machine-learning-results-python.md)
+   [浅谈机器学习的卡方测试](chi-squared-test-for-machine-learning.md)
+   [机器学习中的置信区间](confidence-intervals-for-machine-learning.md)
+   [随机化在机器学习中解决混杂变量的作用](confounding-variables-in-machine-learning.md)
+   [机器学习中的对照试验](controlled-experiments-in-machine-learning.md)
+   [机器学习中的统计学速成课](crash-course-statistics-machine-learning.md)
+   [统计假设检验的临界值以及如何在Python中计算它们](critical-values-for-statistical-hypothesis-testing.md)
+   [如何在机器学习中谈论数据（统计学和计算机科学术语）](data-terminology-in-machine-learning.md)
+   [Python中数据可视化方法的简要介绍](data-visualization-methods-in-python.md)
+   [Python中效果大小度量的温和介绍](effect-size-measures-in-python.md)
+   [估计随机机器学习算法的实验重复次数](estimate-number-experiment-repeats-stochastic-machine-learning-algorithms.md)
+   [机器学习中的评估统计的温和介绍](estimation-statistics-for-machine-learning.md)
+   [如何在Python中计算非参数秩相关性](how-to-calculate-nonparametric-rank-correlation-in-python.md)
+   [如何在Python中计算数据的五个数字摘要](how-to-calculate-the-5-number-summary-for-your-data-in-python.md)
+   [如何在Python中从零开始编写T检验](how-to-code-the-students-t-test-from-scratch-in-python.md)
+   [如何在Python中生成随机数](how-to-generate-random-numbers-in-python.md)
+   [如何转换数据来更好地拟合正态分布](how-to-transform-data-to-fit-the-normal-distribution.md)
+   [如何使用相关来理解变量之间的关系](how-to-use-correlation-to-understand-the-relationship-between-variables.md)
+   [如何使用统计量识别数据中的异常值](how-to-use-statistics-to-identify-outliers-in-data.md)
+   [Python机器学习中的随机数生成器简介](introduction-to-random-number-generators-for-machine-learning.md)
+   [k折交叉验证的温和介绍](k-fold-cross-validation.md)
+   [如何计算McNemar检验来比较两种机器学习分类器](mcnemars-test-for-machine-learning.md)
+   [Python中非参数统计显着性检验简介](nonparametric-statistical-significance-tests-in-python.md)
+   [如何在Python中计算参数统计显着性检验](parametric-statistical-significance-tests-in-python.md)
+   [机器学习中的预测区间](prediction-intervals-for-machine-learning.md)
+   [应用统计学与机器学习的密切关系](relationship-between-applied-statistics-and-machine-learning.md)
+   [如何使用置信区间报告分类器表现](report-classifier-performance-confidence-intervals.md)
+   [统计量分布的简要介绍](statistical-data-distributions.md)
+   [15 个Python中的统计假设检验（备忘单）](statistical-hypothesis-tests-in-python-cheat-sheet.md)
+   [统计假设检验的温和介绍](statistical-hypothesis-tests.md)
+   [10个在机器学习项目中使用统计方法的示例](statistical-methods-in-an-applied-machine-learning-project.md)
+   [Python中统计功效和功效分析的简要介绍](statistical-power-and-power-analysis-in-python.md)
+   [统计采样和重采样的简要介绍](statistical-sampling-and-resampling.md)
+   [用于比较机器学习算法的统计显着性检验](statistical-significance-tests-for-comparing-machine-learning-algorithms.md)
+   [机器学习中统计容差区间的温和介绍](statistical-tolerance-intervals-in-machine-learning.md)
+   [机器学习中的统计书籍](statistics-books-for-machine-learning.md)
+   [评估机器学习模型的统计量](statistics-for-evaluating-machine-learning-models.md)
+   [机器学习中的统计（7天迷你课程）](statistics-for-machine-learning-mini-course.md)
+   [机器学习中的统计简介](statistics-in-plain-english-for-machine-learning.md)
+   [如何使用统计显着性检验来解释机器学习结果](use-statistical-significance-tests-interpret-machine-learning-results.md)
+   [什么是统计（为什么它在机器学习中很重要）？](what-is-statistics.md)
