Sentences with phrase «just noise in the data»

In my world, 0.4 degrees or less of variability is just noise in the data and we spend a lot of time and money to ensure accuracy.

Not only are these short - term «pauses» just noise in the data, but observations show that they are entirley expected, and predicted by climate models (i.e. see Meehl el al. 2011).

If you strip the scientific context of a globally rising temperature trend, you could argue that observed melting of sea ice is just some noise in the data, part of natural variation.

Not exact matches

But LIGO saw only just over one cycle of the Event's ringdown waves before the signal became buried once more in the background noise — not yet enough data to provide a rigorous test of Vishveshwara's predictions.

If our measure was just capturing random noise in the data rather than information about true principal quality, we would not expect it to be related to teacher quality and turnover.

Other data are often just noise: For example, it's interesting that children enrolled in Head Start may be less likely to take to crime as adults, but it's pretty much irrelevant to judging the efficacy of an expensive government program that's failed to show much in terms of student performance.

There's a reason for that: the hockey - stick shaped pattern is in the data, and it's not just noise it's signal.

To get enough signal / noise to detect a trend, you need to consider all hurricane data, not just those landfalling in the US.

(1) In this case even if they were correct and the models failed to predict or match reality (which, acc to this post has not been adequately established, bec we're still in overlapping data and model confidence intervals), it could just as well mean that AGW stands and the modelers have failed to include some less well understood or unquantifiable earth system variable into the models, or there are other unknowns within our weather / climate / earth systems, or some noise or choas or catastrophe (whose equation has not been found yet) thinIn this case even if they were correct and the models failed to predict or match reality (which, acc to this post has not been adequately established, bec we're still in overlapping data and model confidence intervals), it could just as well mean that AGW stands and the modelers have failed to include some less well understood or unquantifiable earth system variable into the models, or there are other unknowns within our weather / climate / earth systems, or some noise or choas or catastrophe (whose equation has not been found yet) thinin overlapping data and model confidence intervals), it could just as well mean that AGW stands and the modelers have failed to include some less well understood or unquantifiable earth system variable into the models, or there are other unknowns within our weather / climate / earth systems, or some noise or choas or catastrophe (whose equation has not been found yet) thing.

Let's compute the warming rate using each 30 - year segment of the Berkeley data, together with the estimated uncertainty in that rate, using an ARMA (1,1) model for the noise just to feed the «uncertainty monster.»

If that is indeed the case then any apparent warming of 0.02 C as shown in graphs 1 and 2 is just noise and there is no evidence of any warming of the Oceans in the ARGO data.

Since year - to - year spikes in the proxy data may just be noise that brings in other confounding factors, scientists average them out to get a nice smooth graph that is meaningful, not on a year - to - year or decade - to - decade level, but on a scale of centuries.

And, this division of the data into gentle steps and jumps is really most likely just seeing patterns in noise.

Since the trend diverges a bit from the data in earlier years and the «system» was possibly a little different then (e.g. higher estimation errors), I'm going to model just a recent noise sample — since 1970.

It can tell you whether or not your «hockey - stick» is a real signal in the data or just an orthogonal component of the noise.

Noise in the data might distract us from seeing this, but Taminos «method» is just flat out dumb.