Short answer: researchers avoid bias in experiments by designing the study so expectations, selection, measurement, and analysis choices are less likely to distort the result.
Bias does not always mean dishonesty. In research, bias means a systematic tendency that pushes results away from the truth. It can enter through who gets selected, who drops out, how outcomes are measured, what researchers expect, and which analyses are reported.
Design tools that reduce bias
Randomization is one of the strongest tools. By assigning participants randomly to groups, researchers reduce the chance that one group is healthier, richer, more motivated, or otherwise different before the experiment begins. Control groups give a comparison point. Placebos help separate the effect of a treatment from the effect of expectation.
Blinding is another major tool. In a single-blind study, participants may not know which group they are in. In a double-blind study, participants and researchers who interact with or assess them may both be unaware. This reduces the risk that hopes, fears, or subtle cues change the result.
Analysis and reporting tools
Bias can also enter after data are collected. Preregistration helps by stating the main hypothesis and analysis plan in advance. Data sharing and code sharing make it easier for others to check the work. Reporting guidelines help ensure that important details are not left out.
No experiment is completely free from bias. The practical question is whether the researchers recognised the main risks and reduced them as much as possible. A trustworthy study is usually one that makes its weaknesses visible rather than pretending they do not exist.