Simulation-Based Inference: Random Sampling vs. Random Assignment? What Instructors Should Know
“Simulation-based inference” is often considered a pedagogical strategy for helping students develop inferential reasoning, for example, giving them a visual and concrete reference for deciding whether the observed statistic is unlikely to happen by chance alone when the null hypothesis is true. In...
Saved in:
| Main Authors: | Beth Chance, Karen McGaughey, Sophia Chung, Alex Goodman, Soma Roy, Nathan Tintle |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-01-01
|
| Series: | Journal of Statistics and Data Science Education |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/26939169.2024.2333736 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Non-Parametric Test for a Two-Way Analysis of Variance
by: Stefano Bonnini, et al.
Published: (2025-03-01) -
Random walks on random networks of cliques: Inferring the network structure
by: Albano Nannini, et al.
Published: (2025-08-01) -
Negation of permutation mass function in random permutation sets theory for uncertain information modeling
by: Yongchuan Tang, et al.
Published: (2024-07-01) -
Ordinal Random Processes
by: Christoph Bandt
Published: (2025-06-01) -
A novel fuzzy inference method for urban incomplete road weight assignment
by: Longhao Wang, et al.
Published: (2024-11-01)