Due to bias, the experimental results can be imperfect or flawed. The people who often design, conduct, and analyze the entire experiment create it with expectations of several desirable results. In other words, they frame a hypothesis and design an experiment in a way that provides the desired outcome. These hope and expectations can interfere with the ways by which an experiment is conducted, experimented with, or interpreted thereby impacting the outcome. This constructed framework is often unintentional and unconscious and is named as experimenter’s bias. There are several types of experimenter bias and the most common ones being observer bias, self-fulfilling prophecy, and interpreter bias. The majority of the modern clinical and social science experiments are designed with several safeguards to decrease the possibility of bias and thereby to avoid distorting results.
Robert Rosenthal, a psychologist in the mid to late 1960s started uncovering the impact of experimenter bias particularly in social science research. One of the most controversial and popular works was in the year 1968 where he studied the expectations of the teachers. During this experiment, Rosenthal and his coworkers provided the students with the standardized intelligence test and later randomly assigned them to a group that was categorized as “intellectual bloomers”. They informed the teachers that these students of these categories were expected to perform well. This experiment was conducted for eight months and the results found the “intellectual bloomers’ to perform well undoubtedly than the other students. However, this suggested that there was an impact on the teacher’s expectation of the students and has affected their performance outcomes. This natural phenomenon where a person’s behavior is molded by the expectations of others is called a Pygmalion effect. This experiment made the scientific fraternity validate their experimental design and methodologies. Few other biases result from the experimenter’s interpretations and observations and not from the interaction of the experimenter with the subjects. Observer bias happens if the experimenter’s preconceptions, assumptions, and prior knowledge impacts the way the experimenter records and observes the experiment. Interpreter bias is a data interpretation error where the experimenter just focuses on one possible interpretation and eliminates the other. To prevent such bias, the majority of the clinical and social science studies are conducted as single-blind studies where the subjects are not revealed if they are participating in the study group or the control group. Several double-blind studies are also conducted where both the subjects and experimenters are not aware of which group, they belong to. Other methods to avoid the experimenter’s bias involve procedures and standardizing ways to decrease the differences in subject and experimenter interactions.
Categories of Experimenter Bias
Modern computerized data and electronic data acquisition techniques have greatly decreased the probability of causing these biases. However, these can still be categorized as poorly designed methods of analysis. It was only during the 1950s and 1960s, that the experimenterâ€™s bias was not recognized. Later on, it became so significant in all the experiments and medical studies. There were about 56 biases that were cataloged by Sackett in the year 1979 that can happen in the measurement of clinical research and sampling. The following are the first few stages of researches and the possible biases that can happen. In reading-up the field:
One-sided reference bias
They all are well literature bias
In specification and sample selection:
Referral filter bias
In executing the experimental exposure:
Withdrawal bias In measuring the outcomes and exposures:
Underlying cause bias
Insensitive measure bias
End-digit preference bias In data analysis:
Data dredging bias
Post-hoc significance bias In the interpretation of the analysis:
Mistaken dissonance bias
Cognitive dissonance bias
Biological and Medical Sciences
The living system and its complexity and the ethical impossibility of performing the fully-controlled experiments with specific species of humans and animals give a rich and difficult experimental bias source. One should have thorough knowledge about the experiment understudy so that they can eliminate the probable reason of bias by identifying the compounding factors is the only way to identify the true cause-effect relationship of the experiment under study. Experimenter bias is always better studied in other branches of sciences.
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