Manipulating Statistics - Essay Example

Manipulating Statistics There are several ways of manipulating statistical data in order to create a favored result especially for adversarial politics on policymakers and vested interests of authors. One of these ways is statistical misrepresentation. The randomness of the sample is very important in getting statistical data from the true population. The research and sampling design should be properly planned to ensure that there is no sampling bias and that the sample is representative of the population.

In the article, Unemployment: How Official Statistics Distort Analysis and Policy, and Why by David Webster, it was noted that the claimant count was misleadingly low because it only included people entitled to unemployment-related benefits. There is a sampling bias in the statement above because it does not consider unemployment under the categories of sickness, government trainees, and discouraged workers. Because the sample was biased, the result and analysis of unemployment may be incorrect.

Another way of manipulating statistics is through spurious correlation. This usually happens when two variables have no direct and causal linked to each other, but both are linked to a third unreported variable. In the article mentioned above, this unauthentic correlation can be observed through the misuse of vacancy data. The job vacancy data may have no direct link with unemployment rate because other variables affect job vacancy such as high turnover rates of some industries, spatial structure of labour market and commuter influx.

The so-called halo effect can also strain the veracity of statistics. The halo effect occurs when the statistics is printed by a well-known source. Just because the source is widely read does not mean it is correct. This effect is observed in the article through the re-use of "workforce rate" where several journalists and local authorities quote from it even if the "workforce rate" is considered invalid.ReferencesGibilisco, S. (2004). Statistics Demystified. Princeton, NJ: McGraw-Hill.