Sentences with phrase «not measurement error»

The samples also aren't independent; the significant noise is natural variation and not measurement error.

For example, temperature variations due to weather are not measurement errors, but they will cause deviations from a linear temperature trend and thus contribute to uncertainty in the underlying trend.

Not exact matches

We should not overreact to a single month's report, since the job numbers are based on a survey and are prone to the measurement errors inherent in any survey.

One diplomat said the IAEA allows for a margin of error of 1 percentage point in such measurements, which means that Iran wasn't technically over the limit.

And in the numerous cases in which exact measurements are not possible one must at least measure the degree of probable error, something that determinists tend to forget.

«I can see that we wouldn't be able to distinguish a few millimetres here and there from measurement error,» says Wikelski.

It's not much — just 15 cm per year — but since that's 100 times greater than the measurement error, something must really be pushing Earth outward.

This new method shows that it is not a factor of measurement error and that the Earth has in fact warmed, said Compo.

These findings may not be generalizable — nearly all the participants were well - educated white adults, and the use of dietary questionnaires and self - reported weight measurement may have introduced measurement errors.

«It is important to note that the exercise data were self - reported and potential measurement error can not be excluded.

Sometimes, these arise because people who commission analytical measurements are ill - informed about the error margins on the data they receive, and do not bother to discover those margins.

There are several reasons for the variation, including whether courts take into account the measurement error inherent in IQ scores — the fact that an individual, tested repeatedly, would not achieve the same score every time, but rather a distribution of scores clustered around their «true» IQ.

Even where measurement error and the Flynn effect are not invoked, a court may have to make sense of a confusing array of results from different tests.

The data is only 33 years in length, but based on that data, there is no first order correlation between temperature and CO2 during its 33 year period and this suggests that then signal to CO2 (ie., Climate Sensitivity) is so low that it can not be measured within the sensitivity, resolution and errors of our best current temperature measurements.

But Barber said there was enough of an error on that measurement that the team members were constantly nervous about the probe not being able to make all of its final maneuvers.

Either way, errors in measurement probably aren't to blame.

We don't know for sure if any change in resting metabolism is because of extra muscle, or whether it's due to measurement error.

Despite the measurement of key confounders in our analyses, the potential for residual confounding can not be ruled out, and although our food frequency questionnaire specified portion size, the assessment of diet using any method will have measurement error.

Even with this methodology and controlling for measurement error and other variables, Krueger and Lindahl found that the effect of the change in schooling on growth did not always pass standard tests for a significant statistical relationship.

However, the fact that we find very «precise zeros» — that is, we don't find statistically significant relationships even though we have the statistical power in our data to detect even very modest relationships — implies that neither measurement error nor a lack of sufficient variation are what's driving our inability to detect a relationship between teaching and research quality.

While numerous papers have highlighted this imprecision, most studies of instability have not systematically considered the role of measurement error in estimates aside from the type that is caused by sampling error.

Accordingly, and also per the research, this is not getting much better in that, as per the authors of this article as well as many other scholars, (1) «the variance in value - added scores that can be attributed to teacher performance rarely exceeds 10 percent; (2) in many ways «gross» measurement errors that in many ways come, first, from the tests being used to calculate value - added; (3) the restricted ranges in teacher effectiveness scores also given these test scores and their limited stretch, and depth, and instructional insensitivity — this was also at the heart of a recent post whereas in what demonstrated that «the entire range from the 15th percentile of effectiveness to the 85th percentile of [teacher] effectiveness [using the EVAAS] cover [ed] approximately 3.5 raw score points [given the tests used to measure value - added];» (4) context or student, family, school, and community background effects that simply can not be controlled for, or factored out; (5) especially at the classroom / teacher level when students are not randomly assigned to classrooms (and teachers assigned to teach those classrooms)... although this will likely never happen for the sake of improving the sophistication and rigor of the value - added model over students» «best interests.»

In 2000, a scoring error by NCS - Pearson (now Pearson Educational Measurement) led to 8,000 Minnesota students being told they failed a state math test when they did not, in fact, fail it (some of those students weren't able to graduate from high school on time).

Perhaps a more reasonable explanation, though, is that there is some bias in the tests upon which the TVAAS scores are measured (as likely related to some likely issues with the vertical scaling of Tennessee's tests, not to mention other measurement errors).

If misalignment is noticed, it is not to be the fault of either measure (e.g., in terms of measurement error), it is to be the fault of the principal who is critiqued for inaccuracy, and therefore (inversely) incentivized to skew their observational data (the only data over which the supervisor has control) to artificially match VAM - based output.

Outliers that are not the result of measurement error are often excluded from analysis about a data set.

I wouldn't read too much into my measurements due to rounding error (I assume you mean I should get 9 - 10.6).

The measurements brought back by Delambre and Méchain not only made science into a global enterprise and made possible our global economy, but also revolutionized our understanding of error.

And since we don't have good ocean heat content data, nor any satellite observations, or any measurements of stratospheric temperatures to help distinguish potential errors in the forcing from internal variability, it is inevitable that there will be more uncertainty in the attribution for that period than for more recently.

The question is: why would the error be in the lower cloud measurements and not the high clouds?

It is not enough merely to republish measured means that are withing the error window of the model, it also needs to account for the error in measurement.

Including arctic measurements increases measurement error spreads and it is the measurement error window that is important here, not the mean.

I don't believe it is the case and I don't think it's statistically viable to change the means of the measurement without acknowledging a change in error window of the measurement.

Standard error involves both natural variability (including that not well understood because it operates on long time scales, and therefore has not been observed during the period of modern technology) as well as measurement error (or error / uncertainty in the proxies).

Given all these measurement issues / fiddles / errors / uncertainties, how certain can we really be that the putative temperature rise is real, anthropogenic or not?

Mine won't change any actual measurement [I did make provision for correcting systemic errors, but following a comment by Nick Stokes I now feel that it is best not to make any changes at all.].

Either climate modelers take proper account of inherent errors (in both measurements and in basic theory) and how they interact within each model or they do not.

It was a battleground since at least 2000, and Douglass et al threw down the guantlet in 2008 (already on line in 2007) demonstrating that models and temperature measurements did not overlap within error bars.

That folks we can not accomplish at all and people who claim it can be done based upon isotopic measurements are in error.

The variance in individual samples is not a result of measurement errors with known Gaussian random noise.

While it is true that there are a host of different things that make up any given individual error estimate at any single point, that does not free us from the constraint imposed by the number of measurements.

If that is the wrong way to interpret measurement error then I don't mind being corrected.

Increasing the number of measurements in this case, does decrease the random component of the instrumental error by 1 / SQRT (n) where n is the the number of observations.

Again, because of the uniform nature of the temperature, a series of n observations taken at various points around the room will have an error that is 1 / SQRT (n) smaller than a single measurement alone.

This noise is either systemic (caused by measurement errors, etc) or aleatory which is contributions from everything else we don't yet fully comprehend, or can't because of the shear number of other paths.

... 2014 Won't Be Statistically Different from 2010 For a «record» temperature to be statistically significant, it has to rise above its level of measurement error, of which there are many for thermometers:... A couple hundredths of a degree warmer than a previous year (which 2014 will likely be) should be considered a «tie», not a record....

Secondly, one of the thing I keep saying when people engage in mathematical calisthenics: «Central values without error bars are not measurements»:

Hence, it possible for a large number of measurements at different locations to result in a meaningful reduction in the level of error of a quantity, provided that the value of the quantity does not vary much across the sample space.

The mass balance determined from a density of 1 to 2 points / km2 (10 and 20 measurement sites) was significantly in error, unlike on Columbia Glacier this error is not consistently negative, overestimating mass balance in 1984 and underestimating mass balance in 1998 (Figure 6).

For the estimation of the total ocean heat content (OHC) a lesser precision would probably be almost as good, because errors of individual measurements always cancel to a large extent as long as the floats do not have common systematic errors.