Sentences with phrase «of measurement bias»

Not exact matches

Gorey KM, Leslie DR: The prevalence of child sexual abuse: Integrative review adjustment for potential response and measurement biases.

But we counted measurement bias when the control group included women who used hydrotherapy during labor, but did not deliver in water, and there was no explanation of why.

Measurement bias was an interesting one for water birth because measurement bias occurs when a study doesn't do a good job of sorting people into the experimental and contMeasurement bias was an interesting one for water birth because measurement bias occurs when a study doesn't do a good job of sorting people into the experimental and contmeasurement bias occurs when a study doesn't do a good job of sorting people into the experimental and control groups.

«By any measurement, «Broken Windows» is racially biased policing, and we are devoted to tarnishing [Bratton's] legacy,» said Robert Gangi, director of the Police Reform Organizing Project advocacy group.

The authors point out that the literature has a number of methodological limitations, such as measurement and selection bias, and a restricted focus, in which the effects of a limited number of alcohol policies are considered without accounting for other alcohol policies.

With Morton, Gould claimed unconscious bias could impact even the most seemingly objective and straightforward part of science: basic measurements and averages.

That bias can influence the sort of secondary work Gould did is a different and a much less surprising claim than unconscious bias influencing actual measurements.

«A wine glass is also a resonant object, so another challenge for us was to make sure that the characteristics of the glass itself weren't biasing our measurements in some way,» said Spratt.

Executive Summary The Berkeley Earth Surface Temperature project was created to make the best possible estimate of global temperature change using as complete a record of measurements as possible and by applying novel methods for the estimation and elimination of systematic biases.

The researchers reason that since each detector's setting is determined by sources that have had no communication or shared history since the beginning of the universe, it would be virtually impossible for these detectors to «conspire» with anything in their shared past to give a biased measurement; the experimental setup could therefore close the «free will» loophole.

Buoys have increased global coverage of the oceans by up to 15 percent since the 1970s, but they have a known cold bias compared to measurements taken from ships.

This bias wears off over time, but the results point to the possibility that measurements of health and well - being, which are vital in making medical assessments and in guiding health - related research, may be misinterpreted.

This kind of update happens all the time as datasets expand through data - recovery efforts and increasing digitization, and as biases in the raw measurements are better understood.

Statisticians can advise on how best to combine data from different sources, how to identify and adjust for biases in different measurement systems, and how to deal with changes in the spatial and temporal coverage of measurements.

... I strongly suspect a systematic bias for undermeasurement of black skulls [during the initial seed - based measurements]» [1].

Using the simulations we also quantify the systematic biases of our shapelet flexion and shear measurement pipeline for deep Hubble data sets such as Galaxy Evolution from Morphology and SEDs, Space Telescope A901 / 902 Galaxy Evolution Survey or the Cosmic Evolution Survey.

This type of measurement error is unlikely to bias our estimates because there is no reason to believe it is related to whether a student won the school - choice lottery.

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).

In response to the need for a measurement of risk that was non biased and separate from the bank, in 1950's FICO (then called Fair Isaac and Company), developed the first credit score but it took over 20 more years to create a successful credit scoring model using data from the three major Credit Reporting Agencies (CRA).

They should both be measuring the same real T, but they will each report different measurements because of the instrument biases.

This kind of update happens all the time as datasets expand through data - recovery efforts and increasing digitization, and as biases in the raw measurements are better understood.

There is a lot of measurement error and there is some «human system bias» dependent on eyesight, tiredness, etcetera, but «subjectivity» is the wrong word.

«However, compensation for a different potential source of bias in SST data in the past decade the transition from ship to buoy - derived SSTs, might increase the century - long trends by raising recent SSTs as much as 0.1 °C, as buoy - derived SSTs are biased cool relative to ship measurements»

The ``... uneven spatial distribution, many missing data points, and a large number of non-climatic biases varying in time and space» all contribute inaccuracies to to the global temperature record — as do errors in orbital decay corrections, limb - corrections, diurnal corrections, and hot - target corrections, all of which rely on measurements (+ - inherent errors), in the satellite temperature records.

«A number of studies have suggested that long - term irradiance - based measurements of cloud cover from satellite may be unreliable due to the inclusion of artifacts, difficulties in observing low - cloud, biases connected to view angles, and calibration issues [1, 2, 3, 4].

An updated Tornetrask chronology, with apparently elevated medieval warmth, turns out to be biased by combining incompatible groups of measurements.

It would be very important to retain all original data so that all runs of the system using adjusted measurements can be compared to runs with the original data in order to quantify the effect of the adjustments and to assist in detecting bias.

Validation of the CO2 inversion product (v16r1): mean bias of the atmospheric component of this product with respect to independent aircraft measurements in the free troposphere.

Pielke Sr., R.A. J. Nielsen - Gammon, C. Davey, J. Angel, O. Bliss, N. Doesken, M. Cai., S. Fall, D. Niyogi, K. Gallo, R. Hale, K.G. Hubbard, X. Lin, H. Li, and S. Raman, 2007a: Documentation of uncertainties and biases associated with surface temperature measurement sites for climate change assessment.

The progressive increase in the ratio of intrinsically warmer (ships) to intrinsically cooler (buoys) measurements introduces a cooling bias in the trend for the combined data.

Use of anomalies also removes any potential systemic bias in those measurements.

3.3.2 In practice, there are many possible sources of uncertainty in a measurement, including: a) incomplete definition of the measurand; b) imperfect reaIization of the definition of the measurand; c) nonrepresentative sampling — the sample measured may not represent the defined measurand; d) inadequate knowledge of the effects of environmental conditions on the measurement or imperfect measurement of environmental conditions; e) personal bias in reading analogue instruments; f) finite instrument resolution or discrimination threshold; g) inexact values of measurement standards and reference materials; h) inexact values of constants and other parameters obtained from external

It's an interesting question, to me, as to whether confirming the bias of those who already biased really is a meaningful or instructive measurement.

The problem with the historical data (besides accuracy and repeatability and quality control questions,...) is that many of the series or single measurements were done at such places like Diekirch, which introduces a strong positive bias.

While these methods are heavily used, there are concerns regarding the distributions of available measurements, how well these sample the globe, and such issues as the degree to which the methods have spatial and seasonal biases or apparent divergence in the relationship with recent climate change.

A significant change in the estimated magnitudes of the biases associated with any measurement type (which may in turn may vary with time or measurement type).

Several researchers have pointed to various other indicators as evidence of «global warming», e.g., Arctic sea ice records, ocean heat content measurements, or animal and plant migration patterns.However, all of these indicators are either too short to compare recent temperatures to temperatures before the 1950s, or else are affected by non-climatic biases.

The hardest uncertainty to deal with is «sounding line bias», arising because the depth of each temperature measurement the Challenger made was taken from the length of rope used.

However, these measurements contain non-negligible random errors and biases owing to the indirect nature of the relationship between the observations and actual precipitation, inadequate sampling, and deficiencies in the algorithms.

Maybe the people doing the measurements should be paying attention to getting their own piece of the science right, and they ought not be giving everyone else cause to wonder if perhaps their own data is extremely inaccurate or biased low.

Key issues identified in inland and marine presentations included the need to standardize the spatial domain, minimize double counting of emissions from lakes and wetlands, reduce bias in field site selections, improve measurements of cold season emissions, and improve scaling of hot spots.

We calculated the mass balance for each measurement network simply from the mean of all the observations, without biasing the results according to the representativeness of the specific sites.

The links between model biases and the underlying assumptions of the shallow cumulus scheme are further diagnosed with the aid of large - eddy simulations and aircraft measurements, and by suppressing the triggering of the deep convection scheme.

Instead of raters, imagine non-destructive measurements of a physical sample taken with an instrument that you later discover may have been biased.

In the case of systematic biases (e.g., a technique that consistently overestimates or underestimates temperature), additional measurements will not cancel the effect.

There is a table summarising estimates of engine room measurement biases in part 2 of our paper which can be obtained here: http://www.metoffice.gov.uk/hadobs/hadsst3/

The measurements from most ships will have some kind of systematic bias, but this will not be exactly the same for all ships, so some component of that will be reduced by averaging the measurements from many ships together.

Bio-optical measurements of chlorophyll from these floats show no significant bias with satellite remote sensing products [Xing et al., 2011].

Human error, random fluctuations, biases, varied weather conditions at times of measurement, limited geographic coverage, missing data...... etc..

Finally, the estimates of biases and other uncertainties presented here should not be interpreted as providing a comprehensive estimate of uncertainty in historical sea - surface temperature measurements.