Improved Graphs for Calculating Sample Sizes When Comparing Two Independent Binomial Distributions

Biometrics ◽  
1979 ◽  
Vol 35 (4) ◽  
pp. 875 ◽  
Author(s):  
John Aleong ◽  
Diantha E. Bartlett
Keyword(s):  
Sample Sizes ◽  
Binomial Distributions

scholarly journals Prevalences of Pospiviroid Contamination in Large Seed Lots of Tomato and Capsicum, and Related Seed Testing Considerations

Viruses ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1034 ◽  
Author(s):  
David Dall ◽  
Lindsay Penrose ◽  
Andrew Daly ◽  
Fiona Constable ◽  
Mark Gibbs
Keyword(s):  
Sample Sizes ◽  
Tomato Seed ◽  
Seed Testing ◽  
Large Seed ◽  
Binomial Distributions ◽  
Commercial Seed ◽  
Larger Sample ◽  
Worthwhile Investment

Analyses of pospiviroids in commercial seed lots of tomato and capsicum, determined by testing of 12,000 to 40,000 seeds per lot, have enabled the development of empirically-derived distribution curves for the observed prevalences of viroids in those commodities. Those distribution curves can be considered in conjunction with statistically-based estimates of detection that would be achieved using other sample sizes. Statistical calculations using binomial distributions show that sample sizes of 3000 and 9400 seeds allow detection of viroid prevalences as low as 0.1% and 0.032%, respectively, with 95% confidence. Applying those calculations to observed viroid prevalences in contaminated tomato seed lots, it is estimated that the use of sample sizes of 3000 and 9400 seeds would detect 15% and 42%, respectively, of the contaminated seed lots identified using the larger sample sizes of approximately 20,000 seeds reported in this study. It is concluded that the higher costs associated with testing of larger sample sizes represent a worthwhile investment in agricultural biosecurity.

An Improved Approximate Formula for Calculating Sample Sizes for Comparing Two Binomial Distributions

Biometrics ◽  
1978 ◽  
Vol 34 (3) ◽  
pp. 483 ◽  
Author(s):  
J. T. Casagrande ◽  
M. C. Pike ◽  
P. G. Smith
Keyword(s):  
Approximate Formula ◽  
Sample Sizes ◽  
Binomial Distributions

Is Red Really Romantic?

2017 ◽  
Vol 48 (3) ◽  
pp. 174-183 ◽  
Author(s):  
Gabrielle K. Lehmann ◽  
Robert J. Calin-Jageman
Keyword(s):  
Opposite Direction ◽  
Positive Control ◽  
Weak Effect ◽  
Sample Sizes ◽  
Quality Controls

Abstract. Red has been reported to enhance attraction for women rating men ( Elliot et al., 2010 ) and men rating women ( Elliot & Niesta, 2008 ). We replicated one of these studies online and in-person. To ensure rigor, we obtained original materials, planned for informative sample sizes, pre-registered our study, used a positive control, and adopted quality controls. For men, we found a very weak effect in the predicted direction (d = 0.09, 95% CI [−0.17, 0.34], N = 242). For women, we found a very weak effect in the opposite direction (d = −0.09, 95% CI [−0.30, 0.12], N = 360). The original studies may have overestimated the red effect, our studies may be an underestimate, or there could be strong moderation of the effect of red on attraction.

Norm-Block Sample Sizes: A Review of 15 Multidimensional Intelligence Tests

2013 ◽  
Author(s):  
Randy G. Floyd ◽  
Ryan L. Farmer ◽  
Sarah Irby ◽  
Phil Norfolk ◽  
Haley Hawkins ◽  
...  
Keyword(s):  
Intelligence Tests ◽  
Sample Sizes ◽  
Block Sample

On the Mathematical Basis of Zelen’s Prerandomized Designs

1985 ◽  
Vol 24 (03) ◽  
pp. 120-130 ◽  
Author(s):  
E. Brunner ◽  
N. Neumann
Keyword(s):  
Clinical Trial ◽  
Normal Distribution ◽  
Random Variables ◽  
Small Samples ◽  
Selection Effects ◽  
Sample Sizes ◽  
Mathematical Basis ◽  
Additional Assumptions

SummaryThe mathematical basis of Zelen’s suggestion [4] of pre randomizing patients in a clinical trial and then asking them for their consent is investigated. The first problem is to estimate the therapy and selection effects. In the simple prerandomized design (PRD) this is possible without any problems. Similar observations have been made by Anbar [1] and McHugh [3]. However, for the double PRD additional assumptions are needed in order to render therapy and selection effects estimable. The second problem is to determine the distribution of the statistics. It has to be taken into consideration that the sample sizes are random variables in the PRDs. This is why the distribution of the statistics can only be determined asymptotically, even under the assumption of normal distribution. The behaviour of the statistics for small samples is investigated by means of simulations, where the statistics considered in the present paper are compared with the statistics suggested by Ihm [2]. It turns out that the statistics suggested in [2] may lead to anticonservative decisions, whereas the “canonical statistics” suggested by Zelen [4] and considered in the present paper keep the level quite well or may lead to slightly conservative decisions, if there are considerable selection effects.

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