Also referred to as Expectation Bias or Observer-Expectancy Effect.

A subset of Confirmation Bias, Expectation Bias is the tendency for a researcher to mainly use data that matches their expectations for the project and to disregard data that conflicts with their expectations. Anytime a researcher removes “unexpected” or outlier data, potentially there is experimenter’s bias at play.

Common Types of Experimenters Bias

• Design: During the design of an experiment, a researcher may form a hypothesis and base their research methodology around their expectations.
• Sampling: Choosing participants in a manner that under or over represents certain demographics.
• Procedural: The way the researcher carries out studies influences the results.
• Interviewer: The way questions are asked or how they are phrased affects how participants answer them.
• Reporting: Presenting the research results in a manner that skews outcomes in the report.

Examples of Experimenters Bias for researchers

• A researcher filtering participants based on preconceived or personal biases.
• Product use cases planned around geographical location (ie, urban vs rural, San Francisco, etc).
• During research, an experimenter may inadvertently introduce bias through interview question phrasing.