Your question is very interesting. As an avid researcher who is in the process of conducting a research project at present, I am faced with assessing bias within my study. Bias occurs when one places the desired emphasis that leads to the desired outcome. Bias can occur accidentally, without a researcher being aware of his or her bias within the study. For example; I am conducting research on the effectiveness of teacher training in implementing behavior plans in a general education setting. I have an extensive background in special education, which could make me lean toward bias. I am already aware of issues I have found among teachers, the classroom, and implementation of behavior plans. As a researcher, I would love to have my results prove that my null hypothesis is invalid.
A little insight: A null hypothesis is taking what one’s hypothesis states and stating the opposite. An example is as follows:
"Hypothesis: Tea drinkers are smarter than coffee drinkers."
"Null hypothesis: Tea drinkers are not smarter than coffee drinkers."
The example that I gave you is very simple, but let us say that you wanted the results to come out in your favor. You decide that when you conduct a study on the literature available, you will only look for articles that support your hypothesis. When you do your experiment, you choose to obtain participants by careful selection who will support the outcome of the study. You study the IQ scores that the participants have after testing. You decide that you should use the IQ scores of participants that have lower scores who drink tea. Then, you choose the participants who had high IQ scores and drank coffee. When the results are analyzed, the results will be biased, and the null hypothesis will be incorrect.
Bias can occur at different stages in a study. One may choose literary support, select participants, misinterpret the outcome of the study, or design the experiment based on bias. Bias can occur on purpose or accidently. One is also at greater risk of bias when conducting a qualitative method study. Therefore, quantitative studies are often considered to have greater validity and reliability.
Perhaps bias will not be exposed in all studies, but when scholars review a study, the bias is often easily identified. Consider researchers conducting research on a new drug that they hope will end Ebola. The researchers may exhibit bias that their drug is highly effective. Their bias hinders the study. The results are flawed, and when the Medical Commission reviews the study, they identify the bias.
“As the primary purpose of scientific publication is to share ideas and new results to foster further developments in the field, the increasing prevalence of fraudulent research and retractions is of concern to every scientist since it taints the whole profession and undermines the basic premise of publishing.”
I would have to say that my perspective identifies the statement as false. Bias in the conduct of a study will make the exposure and outcome of a study stronger within the study. With the millions of dollars invested in research and publishing, editors are becoming more in tune to examples of bias in research.
“Bias in research, where prejudice or selectivity introduces a deviation in the outcome beyond chance, is a growing problem in research.”