Results 211 to 220 of about 6,994,987 (257)
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Automated small sample calorimeter

Review of Scientific Instruments, 1975
We 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, 1990
Abstract 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, 1958
Abstract 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, 1985
AbstractThe 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

1993
Rheological 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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SMALL SAMPLE SIZE SCIENTIST

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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Metabolomics in cancer research and emerging applications in clinical oncology

Ca-A Cancer Journal for Clinicians, 2021
Daniel R Schmidt   +2 more
exaly  

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