Experimenter’s Bias

Details:

The tendency for experimenters to believe, certify, and publish data that agree with their expectations for the outcome of an experiment, and to disbelieve, discard, or downgrade the corresponding weightings for data that appear to conflict with those expectations.

Datasets:

(Datasets labeled with this bias will go here.)

Structural Analysis:

(Results of dataset analysis for patterns in structure, placement, tone, context, coreference, correlatives, and so on will go here.)

Proposed Algorithms:

(Proposed combinations of factors from the above analyses in algorithmic form for automated detection will go here.)

Positive Detection:

(Algorithms for bias-positive detection will go here.)

False-positive Detection:

(Algorithms for false-positive detection/filtering of the above bias-positive algorithms will go here.)

Certainty Mapping:

(Algorithms for the probability mapping between N mathematical dimensions for positive and false-positive algorithms will go here.)

Successful Algorithms:

(Tested successful algorithms will go here, along with their respective accuracy metrics and error data for further refinement.)

References: