Results 251 to 260 of about 9,156,502 (281)
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International Journal of Market Research, 2002
Discusses the dangers of drawing inferences from small samples of data, such as is typically done in qualitative research projects. The problem of ensuring representativeness is discussed: the dangers of convenience samples and the value of purposive sampling.
Timothy Bock, John Sergeant
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Discusses the dangers of drawing inferences from small samples of data, such as is typically done in qualitative research projects. The problem of ensuring representativeness is discussed: the dangers of convenience samples and the value of purposive sampling.
Timothy Bock, John Sergeant
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2015
In a number of application areas, such as materials and genomics, where one wishes to classify objects, sample sizes are often small owing to the expense or unavailability of data points. Many classifier design procedures work well with large samples but are ineffectual or, at best, problematic with small samples.
Lori A. Dalton, Edward R. Dougherty
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In a number of application areas, such as materials and genomics, where one wishes to classify objects, sample sizes are often small owing to the expense or unavailability of data points. Many classifier design procedures work well with large samples but are ineffectual or, at best, problematic with small samples.
Lori A. Dalton, Edward R. Dougherty
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1971
So far we have dealt with the methods of determining the reliability of statistics applicable only to what we have called large samples, and the reader ’s attention has repeatedly been drawn to this limitation. Now the smaller the samples, the more inaccurate become these methods.
T. G. Connolly, W. Sluckin
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So far we have dealt with the methods of determining the reliability of statistics applicable only to what we have called large samples, and the reader ’s attention has repeatedly been drawn to this limitation. Now the smaller the samples, the more inaccurate become these methods.
T. G. Connolly, W. Sluckin
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Small‐sample intervals for regression
Canadian Journal of Statistics, 1992AbstractFor the general linear regression model Y = Xη + e, we construct small‐sample exponentially tilted empirical confidence intervals for a linear parameter 6 = aTη and for nonlinear functions of η. The coverage error for the intervals is Op(1/n), as shown in Tingley and Field (1990).
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Automated small sample calorimeter
Review of Scientific Instruments, 1975We describe an automated calorimetry system for measuring heat capacity in the range 1–35 K. The system employs an on-line computer for signal averaging and data reduction and is capable of performing rapid, accurate specific heat measurements on very small (1–100 mg) samples.
R. E. Schwall +2 more
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Small-Sample Confidence Intervals
Journal of the American Statistical Association, 1990Abstract In this article we present a technique for constructing one- or two-sided confidence intervals, which are second-order correct in terms of coverage, for either parametric or nonparametric problems. The construction is valid in the presence of nuisance parameters.
Maureen Tingley, Christopher Field
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‘Student’ and Small Sample Theory
Journal of the American Statistical Association, 1958Abstract This year marks the Fiftieth Anniversary of the publication of “Student's” distribution. It is an appropriate time to reconsider the impact of this part of “Student's” work on the development of statistics.
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Superfluorescence from Small Samples
Annalen der Physik, 1985AbstractThe initiation of superfluorescence from N ≦ 70 atoms distributed randomly over the volume of a sphere is investigated by numerical computations. When a specified atomic configuration is changed by performing similarity transformations the cooperation number approaches an asymptotic value ≫ 1 for mean distances ≲ λ/50.
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Rheological measurements on small samples
1993Rheological measurements on small samples, but how small is small? In this chapter small means small enough for designing or interpreting bulk flow processes where the fluid may be considered a continuum. Thus, small means the smallest sample one can use that accurately represents the bulk rheological properties. In some cases the effect of sample size
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Pediatrics, 1989
The believer in the law of small numbers practices science as follows: 1. He gambles his research hypotheses on small samples without realizing that the odds against him are unreasonably high. He overestimates power. 2. He has undue confidence in early trends (e.g., the data of the first few subjects) and in the stability ...
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The believer in the law of small numbers practices science as follows: 1. He gambles his research hypotheses on small samples without realizing that the odds against him are unreasonably high. He overestimates power. 2. He has undue confidence in early trends (e.g., the data of the first few subjects) and in the stability ...
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