Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Muundo wa Majaribio ya Msalaba× | Muundo Kamili wa Kiwango cha Majaribio× | |
|---|---|---|
| Nyanja | Muundo wa Majaribio | Muundo wa Majaribio |
| Familia | Hypothesis test | Hypothesis test |
| Mwaka wa asili≠ | 1960 | 1926 |
| Mwanzilishi≠ | Early formalized in clinical research literature; widely used since mid-20th century | R. A. Fisher |
| Aina≠ | Within-subject repeated-measures design | Parametric factorial experiment |
| Chanzo asilia≠ | Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). Wiley. ISBN: 978-0471496533 | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130 |
| Majina mbadala | within-subject crossover, cross-over design, AB/BA design, Çapraz Desen (Crossover Design) | factorial experiment, 2^k factorial, full factorial, Faktöriyel Deneme Deseni (Full Factorial, 2^k) |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | A crossover design is an experimental design in which each participant receives all treatments under investigation, but in a different sequence and across separate time periods. Each subject thus acts as their own control, which substantially reduces between-subject variability and allows efficient treatment comparisons with smaller sample sizes. The approach has been central to clinical pharmacology and comparative research since the mid-20th century, with foundational methodology codified by Senn (2002) and Jones & Kenward (2014). | A full factorial design is a parametric experimental method in which every combination of factor levels is tested simultaneously, enabling the estimation of all main effects and all interaction effects in a single study. Rooted in R. A. Fisher's foundational work on designed experiments (1926) and systematically developed by Box, Hunter, and Hunter (2005) and Montgomery (2017), the 2^k form tests k two-level factors across 2^k experimental runs and is the benchmark against which all other factorial designs are measured. |
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