scholarly journals A large-sample Kolmogorov-Smirnov test for normality of experimental error in a randomized block design

Biometrika ◽  
1978 ◽  
Vol 65 (3) ◽  
pp. 673-676
Author(s):  
CONSTANCE L. WOOD
1957 ◽  
Vol 37 (2) ◽  
pp. 143-151 ◽  
Author(s):  
E. S. Merritt ◽  
J. R. Aitken ◽  
Irene J. Stewart

Data from 14 egg production experiments, which had replicated pens and individual trapnest records, were analysed to obtain estimates of experimental error from which to determine pen effects. The experiments were all nutrition experiments and were conducted at four experimental stations.Analyses were carried out on both a hen-housed and survivor basis. For all experiments the sampling error (individuals) underestimated the experimental error (individuals + pens) by about 20 per cent on a hen-housed basis. On a survivor basis there was no evidence of pen effects, that is, the sampling error did not underestimate the experimental error.The coefficient of variation for all experiments was 40 per cent on a hen-housed basis and 25 per cent on a survivor basis.The relative efficiency of two experimental designs, randomized block and completely randomized, was calculated for 11 of the experiments (on a hen-housed basis only). With the exception of 2 experiments, there was an increased efficiency of up to 500 per cent in utilizing a randomized block design. The data further indicate that the increase in efficiency obtained with a randomized block design is much more marked when the blocks represent different houses than when the blocks consist of different locations within a house.


2007 ◽  
Vol 135 (3) ◽  
pp. 1151-1157 ◽  
Author(s):  
Dag J. Steinskog ◽  
Dag B. Tjøstheim ◽  
Nils G. Kvamstø

Abstract The Kolmogorov–Smirnov goodness-of-fit test is used in many applications for testing normality in climate research. This note shows that the test usually leads to systematic and drastic errors. When the mean and the standard deviation are estimated, it is much too conservative in the sense that its p values are strongly biased upward. One may think that this is a small sample problem, but it is not. There is a correction of the Kolmogorov–Smirnov test by Lilliefors, which is in fact sometimes confused with the original Kolmogorov–Smirnov test. Both the Jarque–Bera and the Shapiro–Wilk tests for normality are good alternatives to the Kolmogorov–Smirnov test. A power comparison of eight different tests has been undertaken, favoring the Jarque–Bera and the Shapiro–Wilk tests. The Jarque–Bera and the Kolmogorov–Smirnov tests are also applied to a monthly mean dataset of geopotential height at 500 hPa. The two tests give very different results and illustrate the danger of using the Kolmogorov–Smirnov test.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sueli Mukai ◽  
Eduardo Mukai ◽  
José Arnaldo Santos-Junior ◽  
Jamil Awad Shibli ◽  
Marcelo Faveri ◽  
...  

Abstract Background Technology advancement has rising in the past decade and brought several innovations and improvements. In dentistry, this advances provided more comfortable and quick procedures to both the patient and the dental surgeon, generating less predictability in the final result. Several techniques has been developed for the preparation of surgical guides aiming at the optimization of surgical procedures. The present study aimed to evaluate the reproducibility and precision of two types of surgical guides obtained using 3D printing and milling methods. Methods A virtual model was developed that allowed the virtual design of milled (n = 10) or 3D printed (n = 10) surgical guides. The surgical guides were digitally oriented and overlapped on the virtual model. For the milling guides, the Sirona Dentsply system was used, while the 3D printing guides were produced using EnvisionTEC’s Perfactory P4K Life Series 3D printer and E-Guide Tint, a biocompatible Class I certified material. The precision and trueness of each group during overlap were assessed. The data were analyzed with GraphPad software using the Kolmogorov–Smirnov test for normality and Student’s t test for the variables. Results The Kolmogorov–Smirnov test showed a normal distribution of the data. Comparisons between groups showed no statistically significant differences for trueness (p = 0.529) or precision (p = 0.3021). However, a significant difference was observed in the standard deviation of mismatches regarding accuracy from the master model (p < 0.0001). Conclusions Within the limits of this study, surgical guides fabricated by milling or prototyped processes achieved similar results.


2010 ◽  
Vol 39 (4) ◽  
pp. 693-704 ◽  
Author(s):  
Zvi Drezner ◽  
Ofir Turel ◽  
Dawit Zerom

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