We should not.
overlook the common flaws that may occur in the process of data collection.
that may flaw the end result as well. Two different types of errors random.
errors and systematic errors alter data conclusions. We will first look at.
random errors. These are errors that are made with similar frequency and in a.
random fashion in either way of the mean. When results that are incorrect and.
errors in data measurement fall on both side of the mean evenly, in the end.
they will balance each other out. For example, in some areas the rate may be.
over reported where in others they may be underreported. The error is.
randomized if we assume that they do balance each other out. However, we.
cannot rely that the uneven distribution will always be equitable. The second.
error is namely, systematic errors. These errors are inconsistent among each.
different reporting system and is of no control of the police or data collectors.
themselves. For example, data collection door to door. A man may fail to.
acknowledge the question correctly and give an answer as to the number of.
members in his household inaccurately. This is uncontrollable of the data.
collector. Another error is a product of the court systems. The courts also.
originate systematic differences. "One cannot compare court statistics from.
place to place without taking into account the nature of the steps that lead to.
cases going to court."5 Different handling operations lead to systematic.
differences. Professional demographers have the ability to deal with.
systematic errors and can adjust the numbers to get a more accurate count. .
However, more accurate, does not mean correct. Subject to the flaws in.
official data we can strongly argue that there is not a guarantee that we can.
rely heavily on official data as a true depiction of the national crime rate, as.
Statistics Canada does. Geographic differences also pose as a problem to the.
verification of decreasing crime.
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