Abstract
Statistical hypothesis testing is among the most misunderstood quantitative analysis methods from data science. Despite its seeming simplicity, it has complex interdependencies between its procedural components. In this paper, we discuss the underlying logic behind statistical hypothesis testing, the formal meaning of its components and their connections. Our presentation is applicable to all statistical hypothesis tests as generic backbone and, hence, useful across all application domains in data science and artificial intelligence.
| Original language | English |
|---|---|
| Pages (from-to) | 945-962 |
| Number of pages | 18 |
| Journal | Machine Learning and Knowledge Extraction |
| Volume | 1 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Sept 2019 |
| Externally published | Yes |
Keywords
- data science
- hypothesis testing
- machine learning
- statistical inference
- statistics
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