Sentences with phrase «data were false positives»

Not exact matches

More disclosure of how data were handled and reported, and making data available, can help other scientists spot false positives in your work.

If the data yield a P value of.05, the risk of a false positive is 26 percent, Colquhoun calculates.

Though they can't tell for sure from the data, the researchers say this suggest that patients who had the thallium scans were sent for further testing using the invasive angiography technique because the initial scan gave a false positive for serious blockage.

While there are a number of portable tests for cocaine commercially available, these are mainly based on antibody reagents, which can not offer quantitative data and — since the cocaine antibody can bind to something that is not cocaine — can give false positive results.

«False positives are going to be random noise rather than systemically biased data.»

The data on the numerous candidates are somewhat preliminary and require validation, but a new analysis by a pair of astrophysicists at the California Institute of Technology suggests that the percentage of false positives among Kepler's candidate planets may be less than 10 percent.

Weeding out false - positives is one of the greatest challenges facing those of us who analyze massively parallel sequencing data.

The transit signals were detected in photometric data from the Kepler satellite, and were confirmed to arise from planets using a combination of large transit - timing variations, radial - velocity variations, Warm - Spitzer observations, and statistical analysis of false - positive probabilities.

Available data suggest few SNP alleles can pass the genome - wide multiple testing criteria and the false positive rate is very high.

Unfortunately, indels are both the strength and the weakness of 454 data — due to the underlying pyrosequencing, homopolymeric regions are often under - or over-called, resulting in numerous false positives.

A combination of sequencing data from two different platforms, as suggested by Nothnagel et al. [5] for the reduction of false positives in newly identified SNVs, is only of limited use for combining the strengths in coverage of different genomic regions.

Data filtering criteria based on the cross correlation score (Xcorr) and delta correlation (ΔCn) values along with tryptic cleavage and charge states were developed using the decoy database approach and applied for filtering the raw data to limit false positive identifications to < 1 % at the peptide level [22]--[Data filtering criteria based on the cross correlation score (Xcorr) and delta correlation (ΔCn) values along with tryptic cleavage and charge states were developed using the decoy database approach and applied for filtering the raw data to limit false positive identifications to < 1 % at the peptide level [22]--[data to limit false positive identifications to < 1 % at the peptide level [22]--[24].

To be fair, however, with all the data rolling in, is it not best to be conservative and limit the number of «false positives» that creep out?

For the LTQ - Orbitrap Velos data, the distribution of mass deviation (from the theoretical masses) was first determined as having a standard deviation (σ) of 2.05 part per million (ppm), and a mass error of smaller than 3σ was used in combination with Xcorr and ΔCn to determine the filtering criteria that resulted in < 1 % false positive peptide identifications.

A false positive rate of < 4 % was estimated for each of the LC - MS data sets.

The algorithm uses the last 1000 data points to not only identify sales, but also try and throw out false positives (when sales rank changes a small amount not due to sales but due to Amazon adjusting the «list» of books being ranked in that category).

To reduce the background (e.g., reduce false positives), it is necessary to collect SNP array data for additional EPI - affected and normal GSDs.

The re-sampling process used to generate the 50 data sets was done to minimize exposure to false positive declarations of significant departures from an OR of 1.0.

However, persistent cloud cover is a continuous issue in the tropics, and extreme flooding can also produce unreliable remotely sensed data that will result in tree cover loss «false positives» (alerts where no actual tree cover loss has occurred).

Critique 1) I have no idea about any cleansing, homogenisation or aggregation performed on this data prior to its presentation by Rutgers 2) Snow extent is only 1 part of the issue, thickness and mass would need to be considered for a full picture 3) I haven't taken care to provide exactly similar sample sizes, however the F and t methods do not require it 4) I haven't taken care to ensure that the same number of winter periods are present in each sample batch; this would increase the risk of a false positive and would have required further investigation if a weak indication of significance had been detected.

Technology has long been used to improve user comprehension of data, but also is prone to oversimplification of the data and a propensity for false positives due to artificial artifacts.

Many of the scales demonstrated weak psychometrics in at least one of the following ways: (a) lack of psychometric data [i.e., reliability and / or validity; e.g., HFQ, MASC, PBS, Social Adjustment Scale - Self - Report (SAS - SR) and all perceived self - esteem and self - concept scales], (b) items that fall on more than one subscale (e.g., CBCL - 1991 version), (c) low alpha coefficients (e.g., below.60) for some subscales, which calls into question the utility of using these subscales in research and clinical work (e.g., HFQ, MMPI - A, CBCL - 1991 version, BASC, PSPCSAYC), (d) high correlations between subscales (e.g., PANAS - C), (e) lack of clarity regarding clinically - relevant cut - off scores, yielding high false positive and false negative rates (e.g., CES - D, CDI) and an inability to distinguish between minor (i.e., subclinical) and major (i.e., clinical) «cases» of a disorder (e.g., depression; CDI, BDI), (f) lack of correspondence between items and DSM criteria (e.g., CBCL - 1991 version, CDI, BDI, CES - D, (g) a factor structure that lacks clarity across studies (e.g., PSPCSAYC, CASI; although the factor structure is often difficult to assess in studies of pediatric populations, given the small sample sizes), (h) low inter-rater reliability for interview and observational methods (e.g., CGAS), (i) low correlations between respondents such as child, parent, teacher [e.g., BASC, PSPCSAYC, CSI, FSSC - R, SCARED, Connors Ratings Scales - Revised (CRS - R)-RSB-, (j) the inclusion of somatic or physical symptom items on mental health subscales (e.g., CBCL), which is a problem when conducting studies of children with pediatric physical conditions because physical symptoms may be a feature of the condition rather than an indicator of a mental health problem, (k) high correlations with measures of social desirability, which is particularly problematic for the self - related rating scales and for child - report scales more generally, and (l) content validity problems (e.g., the RCMAS is a measure of anxiety, but contains items that tap mood, attention, peer interactions, and impulsivity).

These changes are effectively skewing results to the positive side and creating a false, misleading and inaccurate representation of the Calgary market that is causing Calgarians to make financial decisions based on false MLS data.