Sentence examples for more extreme data from inspiring English sources

Rather it is the probability of observing more extreme data than the current data set, given the model assumptions, the posterior distribution of parameters and the discrepancy statistic.

Although most differences were small in the data sets we used, we have reservations on how this will reflect on data sets with more extreme data.

The p value represents the probability of the observed data (or more extreme data) given that the null hypothesis is true: Pr observed data|H0), assuming that the sampling was random and done without error (Kirk, 1996; Johnson, 1999).

The difference between both methods becomes consistently larger for the more extreme conditions of the simulation, indicating that IRT-based plausible value techniques are quite robust against more extreme data situations.

Specifically, Fisher's p-value expresses the probability of the observed and more extreme data given the null hypothesis, and can be considered a random variable whose distribution is uniform on (0, 1) under that null.

However, it did note the day-over-day data may look a little more extreme, and the data may even out a bit with time.

It should be noted that low-angle boundary widths are a fraction of the diameter of SHRIMP pits (Figs 3, 6 and 7), and the actual U-Th enrichment in low-angle boundary domains is more extreme than the data shows due to the homogenization effect of analytical volumes during SHRIMP analysis.

However, this study found the greatest change among those with the highest baseline drinking levels, suggesting potential regression to the mean, which is a statistical phenomenon where more extreme values in data tend to move spontaneously towards the mean over time as a result of a certain amount of natural variation (Barnett et al. 2005).

Note that the differences in growth properties at low and high concentrations of oxygen and glucose are more extreme for the referent data than is seen with other available sets of growth data, such as the data used by [ 2].

Second, the Pathd8 software has been introduced as especially suited for large datasets, being only less precise compared with penalized likelihood methods, but giving more sensible answers for extreme data sets.

The relatively conservative criteria of statistical cut-offs of p≤0.05 (probability of obtaining a result as or more extreme than the observed data) and q≤0.10 (result expected to yield a false discovery rate of no more than 10%) are routinely used in metabolomic studies.