American Journal of Applied Sciences

Biases from Poor Data Analyses

Tshepo Matsose and Solly Matshonisa Seeletse

DOI : 10.3844/ajassp.2016.1033.1039

American Journal of Applied Sciences

Volume 13, Issue 10

Pages 1033-1039


Non-statisticians with little knowledge in basic descriptive statistics tend to think that statistics field is limited to the content to which they are exposed. Many of them believe that a statistical package can augment the little Statistics knowledge they have. They often have a tendency to perform their own data analyses and do not even bounce it against Statistics experts for quality check. Many studies were concluded from data analyses performed by analysts who lack insight into statistical methods. Hence, results in some of their researches have flaws and distorted truths. The paper explains the defects in data analyses and research results that can be caused by influences in the data. Flawed research results may be caused when the data were not scanned for variations and other inconsistencies present in the data. Properly trained statisticians who also understand theories and methods of dealing with outliers can perform these analyses more effectively. However, many researchers fail to seek their advices. This study shows the extent of falsifications that contaminated data can produce and the massive loss to the factualness contained in the data.


© 2016 Tshepo Matsose and Solly Matshonisa Seeletse. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.