Bias is any trend or deviation from the truth in data collection, data analysis, interpretation, and publication that can cause false conclusions. Bias can occur either intentionally or unintentionally. Intention to introduce bias into someone’s research is not moral. Nevertheless, considering the possible consequences of biased research, it is almost equally irresponsible to conduct and publish biased research unintentionally (Gardenier JS, Resnik DB, 2019). Bias distorts the truth, it interferes with the ability to truly understand the environments around us. It is the most challenging obstacle for researchers. It is worth pointing out that every study has its confounding variables and limitations. Confounding effects cannot be completely avoided. While Personal bias happens when the research results are altered due to personal beliefs, customs, attitudes, culture, and errors among many other factors. It also means that the researcher must have analyzed the research data based on his/her beliefs rather than the views perceived by the respondents (Scott K, McSherry R, 2019) In research studies having a well-designed research protocol explicitly outlining data collection and analysis can assist in reducing bias. Feasibility studies are often undertaken to refine protocols and procedures. Bias can be reduced by maximizing follow up and where appropriate in randomized control trials analysis should be based on the intention to treat principle, a strategy that assesses clinical effectiveness because not everyone complies with treatment and the treatment people receive may be changed according to how they respond. Bias research has been criticized for lacking transparency in relation to the analytical processes employed (Smith, J., & Noble, H. 2018).
A quality improvement DPI project could be affected or reduced by the random selection of participants since I am using a clinic setting and in the case of clinical trials randomization of participants into comparison groups. Also, some participants might withdraw from the study or be lost due to failed follow-up. This can result in sample bias or change the characteristics of participants in comparison groups. In qualitative research purposeful sampling has advantages when compared to convenience sampling in that bias is reduced because the sample is constantly refined to meet the study aims. Premature closure of the selection of participants before analysis is complete can threaten the validity of a qualitative study. This can be overcome by continuing to recruit new participants into the study during data analysis until no new information emerges, known as data saturation.
References
Gardenier JS, Resnik DB. The misuse of statistics: concepts, tools, and a research agenda. Account Res. 2019;9:65–74. http://dx.doi.org/10.1080/08989620212968. [PubMed] [Google Scholar]
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