Fixing the analysis before looking at the answer, so that the result cannot be tuned towards the expected one.Blind analysis is a methodological safeguard used in experimental science to prevent the researcher's expectations from influencing the outcome of the analysis. The idea is to fix the entire analysis protocol — including all data-processing steps, statistical tests, and selection criteria — before examining the actual data. This prevents the common pitfall of 'p-hacking' or unconsciously tweaking the analysis until a desired result emerges.
The technique was first formalized in particle physics, notably by the BaBar collaboration in the late 1990s, and has since spread to other fields.
In practice, the researcher designs the full analysis plan in advance, often with the help of a colleague who does not know the true answer. The actual data is then 'blinded' — for example, by adding a random offset to the key measurement or by hiding the labels of the experimental groups. The analysis is run on the blinded data, and only after the analysis is complete and the results are recorded is the blinding removed. This ensures that the final result is not influenced by the researcher's prior beliefs or hopes.
It is used in high-energy physics experiments (e.g., at CERN), clinical trials, and psychology to ensure that results are not biased by the experimenter's expectations.
In some blind analyses, the data is 'hidden' by adding a secret random number to all measurements; the analysis is done on the shifted data, and only at the very end is the shift subtracted to reveal the true result.
