Significance of Statistical design
Statistical design refers to structured methods utilized for analyzing data from experiments and studies, including statistical tests like the paired 't' test and chi-square test to evaluate treatment effects. It encompasses the methodology for analyzing clinical study data to determine treatment efficacy and involves systematic planning of experiments to optimize process variables and assess their effects on product characteristics. This approach is crucial for ensuring reliable and valid conclusions in research and development.
Synonyms: Experimental design, Survey design, Research design, Sampling design, Statistical methodology.
The below excerpts are indicatory and do represent direct quotations or translations. It is your responsibility to fact check each reference.
Hindu concept of 'Statistical design'
Statistical design in Hinduism involves analytical methods, such as paired 't' tests and Wilcoxon signed rank tests, to evaluate the effectiveness of Kshara Sutra based on various parameters, demonstrating a methodological approach to religious practices.
From: Journal of Ayurveda and Integrated Medical Sciences
(1) Statistical design details the tests used to analyze the data, including Wilcoxon signed rank test and Mann Whitney 'U' test.[1] (2) This refers to the methods used to analyze the data, including paired 't' tests of significance, to assess the efficacy of Kshara Sutra based on various parameters.[2]
The concept of Statistical design in scientific sources
Statistical design encompasses structured methods for analyzing experimental and study data, such as paired 't' and chi-square tests, enabling assessment of treatment effects and evaluating efficacy in clinical studies.
From: Journal of Public Health in Africa
(1) This refers to the methods used for analyzing the study's data, including frequency, percentile, mean, standard deviation, and various tests to determine significant differences.[3]
From: International Journal of Pharmacology
(1) Experimental approaches used to efficiently identify key factors influencing a process from a multivariable system, such as full factorial or Plackett-Burman design.[4]
From: Asian Journal of Pharmaceutics
(1) A structured approach to plan experiments and analyze data.[5]