Atheists are not to be trusted

[Hobbitgirl]

Atheists identified as America's most distrusted minority, according to new U of M study



http://www1.umn.edu/news/news-releases/2..._2816.html

[fstratzero]

(29-01-2013 06:33 PM)Hobbitgirl Wrote:  Atheists identified as America's most distrusted minority, according to new U of M study



http://www1.umn.edu/news/news-releases/2..._2816.html

I'm always skeptical about studies...

http://en.wikipedia.org/wiki/Selection_bias
http://en.wikipedia.org/wiki/Experimenter%27s_bias

Sampling bias

Sampling bias is systematic error due to a non-random sample of a population,[2] causing some members of the population to be less likely to be included than others, resulting in a biased sample, defined as a statistical sample of a population (or non-human factors) in which all participants are not equally balanced or objectively represented.[3] It is mostly classified as a subtype of selection bias,[4] sometimes specifically termed sample selection bias,[5][6] but some classify it as a separate type of bias.[7]

A distinction, albeit not universally accepted, of sampling bias is that it undermines the external validity of a test (the ability of its results to be generalized to the rest of the population), while selection bias mainly addresses internal validity
for differences or similarities found in the sample at hand. In this
sense, errors occurring in the process of gathering the sample or cohort
cause sampling bias, while errors in any process thereafter cause
selection bias.

Examples of sampling bias include self-selection,
pre-screening of trial participants, discounting trial subjects/tests
that did not run to completion and migration bias by excluding subjects
who have recently moved into or out of the study area.

Time interval

• Early termination of a trial at a time when its results support a desired conclusion.
• A trial may be terminated early at an extreme value (often for ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all variables have a similar mean.

Exposure

• Susceptibility bias
  • Clinical susceptibility bias, when one disease predisposes
    for a second disease, and the treatment for the first disease
    erroneously appears to predispose to the second disease. For example, postmenopausal syndrome gives a higher likelihood of also developing endometrial cancer, so estrogens given for the postmenopausal syndrome may receive a higher than actual blame for causing endometrial cancer.[8]
  • Protopathic bias, when a treatment for the first symptoms of a
    disease or other outcome appear to cause the outcome. It is a potential
    bias when there is a lag time from the first symptoms and start of
    treatment before actual diagnosis.[8] It can be mitigated by lagging, that is, exclusion of exposures that occurred in a certain time period before diagnosis.[9]
  • Indication bias, a potential mix up between cause and effect
    when exposure is dependent on indication, e.g. a treatment is given to
    people in high risk of acquiring a disease, potentially causing a
    preponderance of treated people among those acquiring the disease. This
    may cause an erroneous appearance of the treatment being a cause of the
    disease.[10]

Data

• Partitioning data with knowledge of the contents of the partitions,
  and then analyzing them with tests designed for blindly chosen
  partitions.
• Rejection of "bad" data on arbitrary grounds, instead of according to previously stated or generally agreed criteria.
• Rejection of "outliers" on statistical grounds that fail to take
  into account important information that could be derived from "wild"
  observations.[11]

Studies

• Selection of which studies to include in a meta-analysis (see also combinatorial meta-analysis).
• Performing repeated experiments and reporting only the most
  favorable results, perhaps relabelling lab records of other experiments
  as "calibration tests", "instrumentation errors" or "preliminary
  surveys".
• Presenting the most significant result of a data dredge as if it were a single experiment (which is logically the same as the previous item, but is seen as much less dishonest).

Attrition

Attrition bias is a kind of selection bias caused by attrition (loss of participants),[12] discounting trial subjects/tests that did not run to completion. It includes dropout, nonresponse (lower response rate), withdrawal and protocol deviators.
It gives biased results where it is unequal in regard to exposure
and/or outcome. For example, in a test of a dieting program, the
researcher may simply reject everyone who drops out of the trial, but
most of those who drop out are those for whom it was not working.
Different loss of subjects in intervention and comparison group may
change the characteristics of these groups and outcomes irrespective of
the studied intervention.[12]

Observer selection

Data is filtered not only by study design and measurement, but by the
necessary precondition that there has to be someone doing a study. In
situations where the existence of the observer or the study is
correlated with the data observation selection effects occur, and anthropic reasoning is required.[13]

An example is the past impact event
record of Earth: if large impacts cause mass extinctions and ecological
disruptions precluding the evolution of intelligent observers for long
periods, no one will observe any evidence of large impacts in the recent
past (since they would have prevented intelligent observers from
evolving). Hence there is a potential bias in the impact record of
Earth.[14] Astronomical existential risks might similarly be underestimated due to selection bias, and an anthropic correction has to be introduced.[15]

Avoidance

In the general case, selection biases cannot be overcome with statistical analysis of existing data alone, though Heckman correction
may be used in special cases. An informal assessment of the degree of
selection bias can be made by examining correlations between exogenous (background) variables and a treatment indicator. However, in regression models, it is correlation between unobserved determinants of the outcome and unobserved
determinants of selection into the sample which bias estimates, and
this correlation between unobservables cannot be directly assessed by
the observed determinants of treatment.[16]

Now I've experienced people who cannot imagine trusting somebody who doesn't have the fear of hell. Which I find to be sick and twisted. You can trust a person who wishes to do good within set limits, granted you have to find out what their limits are and not short cut your self to the ten commandments.

[Full Circle]

(29-01-2013 06:33 PM)Hobbitgirl Wrote:  Atheists identified as America's most distrusted minority, according to new U of M study

Not surprising. I read recently that an atheist is the least likely to be voted into public office.

Take the recent inauguration, for a country with an Establishment Clause didn't the President's inaugural speech contain a bit too much religious woo?

Here is a recent PEW survey regarding Religion and Politics

http://www.pewforum.org/Politics-and-Ele...itics.aspx

BTW in the study you cite the line that most pisses me off is “Our findings seem to rest on a view of atheists as self-interested individuals who are not concerned with the common good.”

[That Icelandic]

People are afraid of clear thinking and facing reality. They like sugarcoating.

[Adenosis]

Oh no! Atheists! Ruuuun!

(29-01-2013 10:27 PM)That Icelandic Wrote:  People are afraid of clear thinking and facing reality. They like sugarcoating.

Indeed, some people find fairy tales are much more comforting than reality.

[That Icelandic]

Do you need to get the app for this to be able to get rated or whats the deal

[morondog]

(29-01-2013 10:59 PM)That Icelandic Wrote:  Do you need to get the app for this to be able to get rated or whats the deal

Ratings will come padewan You gotta impress someone so much that they loooooooooooove you so much as to give you a meaningless number + comment. I'd do it but I don't wanna Stick around a bit though, and sooner or later people will start piling them on.