The scientific method requires that we be able to eliminate factors such
as observer bias, for example.
Those inconsistencies may be due to factors such
as observer bias in a laboratory setting and small sample sizes.
Not exact matches
As civil rights groups accuse the educational system of racial
bias, some
observers argue that implementation of less - punitive measures of discipline may make schools more unsafe.
Researchers concluded that because districts «do not have processes in place to address the possible
biases in observational scores,» statistical adjustments might be made to offset said
bias,
as might external
observers / raters be brought in to yield more «objective» observational assessments of teachers.
When discussing various
observers» attitudes towards some body of objective data, such
as the data underlying the science of climate change, those who evaluate the data without undue ideological
bias might better be described
as «impartial» rather than
as «objective».
I suppose it's relevant to the current issue
as an illustration of confirmation
bias — i.e., a belief in the objective truth of something that most knowledgeable
observers would see
as seriously distorted.
As the Australian blogger Joanne Nova summarised Tol's findings, John Cook of the University of Queensland and his team used an unrepresentative sample, left out much useful data, used biased observers who disagreed with the authors of the papers they were classifying nearly two - thirds of the time, and collected and analysed the data in such a way as to allow the authors to adjust their preliminary conclusions as they went along, a scientific no - no if ever there was on
As the Australian blogger Joanne Nova summarised Tol's findings, John Cook of the University of Queensland and his team used an unrepresentative sample, left out much useful data, used
biased observers who disagreed with the authors of the papers they were classifying nearly two - thirds of the time, and collected and analysed the data in such a way
as to allow the authors to adjust their preliminary conclusions as they went along, a scientific no - no if ever there was on
as to allow the authors to adjust their preliminary conclusions
as they went along, a scientific no - no if ever there was on
as they went along, a scientific no - no if ever there was one.
Some may be subject to
observer biases and may be from indirect (proxy) measurements of cloud, such
as sunshine measurements or DTR variations.
While some variance of this is expected,
as observers won't reset the instrument at the same time every day, these departures should be mostly random and won't necessarily introduce systemic
bias.
The whole POINT of sharing data is so that other scientists can eliminate
observer bias (motivation)
as a source of error in the analysis.