Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 1 |
| Since 2007 (last 20 years) | 1 |
Descriptor
| Attribution Theory | 1 |
| Comparative Analysis | 1 |
| Correlation | 1 |
| Data Interpretation | 1 |
| Inferences | 1 |
| Introductory Courses | 1 |
| Regression (Statistics) | 1 |
| Simulation | 1 |
| Statistical Bias | 1 |
| Statistics | 1 |
| Teaching Methods | 1 |
| More ▼ | |
Source
| Journal of Statistics… | 1 |
Publication Type
| Journal Articles | 1 |
| Reports - Descriptive | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Lübke, Karsten; Gehrke, Matthias; Horst, Jörg; Szepannek, Gero – Journal of Statistics Education, 2020
Basic knowledge of ideas of causal inference can help students to think beyond data, that is, to think more clearly about the data generating process. Especially for (maybe big) observational data, qualitative assumptions are important for the conclusions drawn and interpretation of the quantitative results. Concepts of causal inference can also…
Descriptors: Inferences, Simulation, Attribution Theory, Teaching Methods

Peer reviewed
Direct link
