Statisticshave been believed to be one of the most transparent ways ofpresenting information. This is due to the ethics in reportingstatistical data. However, it is possible to misrepresent data andconclusions using statistics.
Statisticscan present both faulty and accurate information, which can be usedin making conclusions. In evaluating the accuracy of statisticaldata, it is critical to look at the process of creating thestatistics. In the creation process, data is usually collected,interpretation is done, and then it is presented. Mistakes can occurat any level during construction. Therefore, because of the differenttypes of errors that may occur, statistics can become flawed. Instatistics, sampling errors can occur, where a small portion of theentire population does not exactly represent the properties orcharacteristics of the entire population (Groves, 2004). In casethis error occurs, statistics would make the wrong conclusions. Insuch a case, the statistics would be used to misrepresent data andconclusions.
Biasis likely to happen in statistics. Individuals conducting surveys areusually in control concerning who or what to include in thecollection of data (Best, 2001). Therefore, in case theseindividuals want to prove a certain point, they may be drawn tosamples that would more likely present figures supporting theirposition. In such a scenario, the statistics would be used in makingconclusions that represent the perspective of the researchers, butnot the true view of the population.
Onthe other hand, manipulation of data may also lead to statisticsproviding a conclusion that bears no truth. Individuals can lie instatistics through making up data or altering the data (Best, 2001).Such numbers can misrepresent the true data and conclusionstherefore, it is possible to misrepresent data and conclusions usingstatistics.
Best, Joel.  (2001). Damned Lies and Statistics. Berkeley: University of  California Press.
Groves, Robert. (2004). Survey Errors and Survey Costs. New York:Wiley-Interscience.