Biases from poor data analyses

Tshepo Matsose, Solly Matshonisa Seeletse*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)1033-1039
Number of pages7
JournalAmerican Journal of Applied Sciences
Issue number10
Publication statusPublished - 5 Oct 2016


  • Data variations
  • Information falsification
  • Statistical falsehood


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