Comparison Between Balanced and Random Arrangements of Field Plots

Student,

doi:10.2307/2332011

Cited by 54 publications

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“…Gosset (writing as `Student') never accepted Fisher's arguments for randomization in agricultural field trials and argued convincingly that his own nonrandom designs for the placement of treatment and controls yielded more precise estimates of treatment effects (see Student (1938) and Ziliak (2014)). Gosset worked for Guinness where inefficiency meant lost revenue, so he had reasons to care, as should we.…”

Section: Introductionmentioning

confidence: 99%

Understanding and Misunderstanding Randomized Controlled Trials

Deaton¹,

Cartwright²

2016

166

151

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RCTs would be more useful if there were more realistic expectations of them and if their pitfalls were better recognized. For example, and contrary to many claims in the applied literature, randomization does not equalize everything but the treatment across treatments and controls, it does not automatically deliver a precise estimate of the average treatment effect (ATE), and it does not relieve us of the need to think about (observed or unobserved) confounders. Estimates apply to the trial sample only, sometimes a convenience sample, and usually selected; justification is required to extend them to other groups, including any population to which the trial sample belongs. Demanding "external validity" is unhelpful because it expects too much of an RCT while undervaluing its contribution. Statistical inference on ATEs involves hazards that are not always recognized. RCTs do indeed require minimal assumptions and can operate with little prior knowledge. This is an advantage when persuading distrustful audiences, but it is a disadvantage for cumulative scientific progress, where prior knowledge should be built upon and not discarded. RCTs can play a role in building scientific knowledge and useful predictions but they can only do so as part of a cumulative program, combining with other methods, including conceptual and theoretical development, to discover not "what works," but "why things work".

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Section: Introductionmentioning

confidence: 99%

Understanding and Misunderstanding Randomized Controlled Trials

Deaton¹,

Cartwright²

2016

166

151

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“…Who is this invisible giant with a hero's cape? "Student" is the name which graces Student's t, the most popular test of statistical significance in the world, and the foundation from which Fisher's p-values were originally derived (Student 1908a(Student , 1925Fisher 1925;Ziliak 2008). Most statisticians have heard that "Student" is in reality the pen name of William Sealy Gosset (1876Gosset ( -1937, a brewer of Guinness beer.…”

Section: How a Giant Statistician And Brewer Sidestepped The Crisis Omentioning

confidence: 99%

How Large Are Your G -Values? Try Gosset’s Guinnessometrics When a Little “ p ” Is Not Enough

Ziliak

2019

The American Statistician

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A crisis of validity has emerged from three related crises of science, that is, the crises of statistical significance and complete randomization, of replication, and of reproducibility. Guinnessometrics takes commonplace assumptions and methods of statistical science and stands them on their head, from little p-values to unstructured Big Data. Guinnessometrics focuses instead on the substantive significance which emerges from a small series of independent and economical yet balanced and repeated experiments. Originally developed and market-tested by William S. Gosset aka "Student" in his job as Head Experimental Brewer at the Guinness Brewery in Dublin, Gosset's economic and common sense approach to statistical inference and scientific method has been unwisely neglected. In many areas of science and life, the 10 principles of Guinnessometrics or G-values outlined here can help. Other things equal, the larger the G-values, the better the science and judgment. By now a colleague, neighbor, or YouTube junkie has probably shown you one of those wacky psychology experiments in a video involving a gorilla, and testing the limits of human cognition. In one video, a person wearing a gorilla suit suddenly appears on the scene among humans, who are themselves engaged in some ordinary, mundane activity such as passing a basketball. The funny thing is, prankster researchers have discovered, when observers are asked to think about the mundane activity (such as by counting the number of observed passes of a basketball), the unexpected gorilla is frequently unseen (for discussion see Kahneman 2011). The gorilla is invisible. People don't see it.

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“…Thus, Student (1938), in a posthumous paper in which he argued strongly against the use of randomization in the design of agricultural variety trials, emphasized the importance of randomization when there was the possibility of observer's and other errors arising from personal differences or selection effects.…”

Section: Purposes Of Randomizationmentioning

confidence: 99%

“…The interchange between Student (1938) and Fisher about the merits of randomized as contrasted with systematic designs in agricultural variety trials centred essentially on the extent to which randomized designs could achieve balance with respect to plausible patterns of variability. Yates (1939), at the conclusion of the discussion, suggested that, while there might sometimes be small gains in precision to be achieved by systematic arrangements, the lack of security in the basis for error estimation in such designs distracted attention from key issues of interpreting the effects under study.…”

Section: Analysis Of Covariancementioning

confidence: 99%

Randomization in the Design of Experiments

Cox¹

2009

Int Statistical Rev

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A general review is given of the role of randomization in experimental design. Three objectives are distinguished, the avoidance of bias, the establishment of a secure base for the estimation of error in traditional designs, and the provision of formally exact tests of significance and confidence limits. The approximate randomization theory associated with analysis of covariance is outlined and conditionality considerations are used to explain the limited role of randomization in experiments with very small numbers of experimental units. The relation between the so-called design-based and model-based analyses is discussed. Corresponding results in sampling theory are mentioned briefly. Copyright (c) 2009 The Author. Journal compilation (c) 2009 International Statistical Institute.

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Comparison Between Balanced and Random Arrangements of Field Plots

Cited by 54 publications

References 0 publications

Understanding and Misunderstanding Randomized Controlled Trials

Understanding and Misunderstanding Randomized Controlled Trials

How Large Are Your G -Values? Try Gosset’s Guinnessometrics When a Little “ p ” Is Not Enough

Randomization in the Design of Experiments

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