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At least 307 records · Page 17Linked to original sources
PubMed2001

Prediction of crude protein and amino acid passage to the duodenum of lactating cows by models compared with in vivo data.

To determine whether statistical inferences obtained from predictions by models were similar to those of measured data from individual cows, data from six research trials published between 1989 and 1997 were simulated using the 1989 National Research Council Model, the Mepron Dairy Ration Evaluator (version 1.1), The University of Pennsylvania release of the Net Carbohydrate and Protein System (version 2.12p), The Cornell Net Carbohydrate and Protein System (version 3), and the CPM Dairy (version 1.0). Both predicted and measured protein fractions were analyzed by ANOVA and compared to determine whether statistical inferences among treatments from predictions by the models were similar to those from the measured data. The interpretations and statistical inferences of measured data did not always agree with those for predicted data. All models responded to changes in diet composition and often predicted that dietary changes would result in statistically different amounts of protein and amino acids passing to the duodenum than were observed in the measured data. The direction of predicted change among treatments for passage of nitrogen fractions to the duodenum also did not agree with the measured data a large percentage of the time. Discrepancies in ANOVA and interpretations between predicted and measured data may be due to the reduction in variation associated with modeling biological systems, associative effects of feeds not accounted for by models, inadequate equations in the models, inadequate description of feeds, or experimental error in measured data. Before model simulations of duodenal flow of crude protein and amino acids can be substituted for experimental measurements, better descriptors of main dietary effects, microbial protein production, ruminal protein degradation, and interactions among dietary factors must be developed.

Amino Acids↗
PubMed2006

Comparison of statistical significance criteria.

We study and compare two classes of statistical criteria to assess the significance of exceptional words. Indeed, the Z-score-like criteria, or the normal approximation that is a strict equivalent, suffer from several drawbacks in terms of sensitivity and specificity. Thanks to the combinatorial structure of words, a computation of the exact P-value has been made possible by recent mathematical results. We study here the drawbacks of the Z-score, the choice of the threshold and the tightness to the P-value. A major conclusion is that the normal approximation is always very poor and overestimates statistical significance.

Algorithms↗
PubMed2006

Frequency distribution of TATA Box and extension sequences on human promoters.

BACKGROUND: TATA box is one of the most important transcription factor binding sites. But the exact sequences of TATA box are still not very clear. RESULTS: In this study, we conduct a dedicated analysis on the frequency distribution of TATA Box and its extension sequences on human promoters. Sixteen TATA elements derived from the TATA Box motif, TATAWAWN, are classified into three distribution patterns: peak, bottom-peak, and bottom. Fourteen TATA extension sequences are predicted to be the new TATA Box elements due to their high motif factors, which indicate their statistical significance. Statistical analysis on the promoters of mice, zebrafish and drosophila melanogaster verifies seven of these elements. It is also observed that the distribution of TATA elements on the promoters of housekeeping genes are very similar with their distribution on the promoters of tissue specific genes in human. CONCLUSION: The dedicated statistical analysis on TATA box and its extension sequences yields new TATA elements. The statistical significance of these elements has been verified on random data sets by calculating their p values.

Base Sequence↗
PubMed1989

Dual controls, p-value plots, and the multiple testing issue in carcinogenicity studies.

The interpretation of statistically significant findings in a carcinogenicity study is difficult, in part because of the large number of statistical tests conducted. Some scientists who believe that the false positive rates in these experiments are unreasonably large often suggest that the use of multiple control groups will provide important insight into the operational false positive rates. The purpose of this paper is 2-fold: to present results from two carcinogenicity studies with dual control groups, and to present and illustrate a new graphical technique potentially useful in the analysis and interpretation of tumor data from carcinogenicity studies. The experimental data analyzed show that statistically significant differences between identically treated groups will occur with regular frequency. Such data, however, do not provide strong evidence of extrabinomial variation in tumor rates. The p-value plot is advocated as a graphical method that can be used to assess visually the ensemble of p values for neoplasm data from an entire study. This technique is then illustrated using several examples. Through computer simulation, we present p-value plots generated with and without treatment effects present. On average, the plots look substantially different depending on the presence or absence of an effect. We also evaluate decision rules motivated by the p-value plots. Such rules appear to have good power to detect treatment effects (i.e., have low false negative rates) while still controlling false positive rates.

Animals↗
PubMed2003

Further statistics in dentistry. Part 9: Bayesian statistics.

Statistics can be defined as the methods used to assimilate data, so that guidance can be given, and conclusions drawn, in situations which involve uncertainty. In particular, statistical inference is concerned with drawing conclusions about particular aspects of a population when that population cannot be studied in full. Uncertainty arises here because the totality of the information is not available. Instead, to make inferences about the population, it is necessary to rely on a sample of data which is selected from the population; this sample data may be augmented, in certain circumstances, by auxiliary information which is obtained independently of the sample data. Clearly, uncertainty lies at the heart of statistics and statistical inference. This uncertainty is measured by a probability which therefore forms the crux of statistics and must be properly understood in order to interpret a statistical analysis.

Algorithms↗
PubMed1996

Human performance and physiology: a statistical power analysis of ELF electromagnetic field research.

Research examining the effects of electromagnetic fields (EMFs) on human performance and physiology has produced inconsistent results; this might be attributable to low statistical power. Statistical power refers to the probability of obtaining a statistically significant result, given the fact that a real effect exists. The results of a survey of published investigations of the effects of EMFs on human performance and physiology show that statistical power levels are very low, ranging from a mean of .08 for small effect sizes to .46 for large effect sizes. Implications of these findings for the interpretation of results are discussed along with suggestions for increasing statistical power.

Behavior↗