da Data interpretation is a component of modern life for most people. Interpretation is the
mechanism for translating all the numerical data that we are bombarded with every minute of
every day. Consumers interpret data when they turn on the television, scan headlines on an
iPhone or tablet, view advertisements alleging that one product is superior to another or they
make purchases based on advertising as to the price and/or efficacy of a product.
A prevailing method of analyzing numerical data is known as statistical analysis and the
activity associated with assessing and explaining data in order to make predictions is referred
to as inferential ...view middle of the document...
This is just a single example of how data gathering and the interpretation in
just one arena can have enormous impact on the entire country.
In research, bias refers to inaccuracies or mistakes that crop up consistently in research
analysis. By definition, bias is any propensity which hinders unprejudiced consideration of a
question. In general, bias happens when systematic miscalculation is entered into sampling or
testing by choosing or promoting one result or outcome over others. Bias can materialize from
the method used, samples selected for the research or anything that may impact the findings
positively or negatively. Bias may also manifest through publishers and organizations who
furnish funding or report the research. Bias may be deliberate or unintended; if any form of
bias was deliberate the researcher will typically stipulate it in the conclusions portion of his
Sampling bias occurs when the samples of an aleatory variable are collected
to ascertain its distribution are chosen incorrectly and fail to represent the true distribution as a
result of non-random reasons. Ponder the following example. Assume we want to predict the
winner of a presidential race by method of an opinion poll. Querying one thousand registered
voters as to their expected choices would seem to offer a fairly precise prediction of the
eventual winner, but only if our sample is “representative” of the electorate in its entirety . If
the sample consists of only white middle class college students, then the opinions of significant
portions of the electorate such as ethnic minorities, the elderly, and blue- collar workers are
grossly underrepresented and our ability to accurately predict the outcome from that sample is
In an unbiased sample, variations between the samples selected from a random variable
and its true distribution, or variations between the samples of units from a population and the
entire population they embody, should occur only from chance. If the variations are not just
the result of chance, then there is a sampling bias. Sampling bias often emerges because certain
values of the variable are methodically underrepresented or overrepresented in regard to the
true distribution of the variable. Because of its undeviating nature, sampling bias results in a
systematic slant of the approximation of the sampled probability distribution.This slant cannot
be deleted simply by enlarging the size of the sample. Polling an additional thousand white
college students will not enhance the predictive capacity , but querying one
thousand selected at random from the electoral register would.
A typical source of sampling or selection bias may be found in the blueprint of the study
or in the data collection process, both of which may influence or discourage collecting data
from certain groups or individual members of the groups or in certain...