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There are phenomena that cannot be measured without subjective testing.However, subjective testing is a complex issue with many influencing factors.These interplay to yield either precise or incorrect results. Researchersrequire a tool to classify results of subjective experiment as eitherconsistent or inconsistent. This is necessary in order to decide whether totreat the gathered scores as quality ground truth data. Knowing if subjectivescores can be trusted is key to drawing valid conclusions and buildingfunctional tools based on those scores (e.g., algorithms assessing theperceived quality of multimedia materials). We provide a tool to classifysubjective experiment (and all its results) as either consistent orinconsistent. Additionally, the tool identifies stimuli having irregular scoredistribution. The approach is based on treating subjective scores as a randomvariable coming from the discrete Generalized Score Distribution (GSD). TheGSD, in combination with a bootstrapped G-test of goodness-of-fit, allows toconstruct $p$-value P-P plot that visualizes experiment's consistency. The toolsafeguards researchers from using inconsistent subjective data. In this way, itmakes sure that conclusions they draw and tools they build are more precise andtrustworthy. The proposed approach works in line with expectations drawn solelyon experiment design descriptions of 21 real-life multimedia quality subjectiveexperiments.

Describing Subjective Experiment Consistency by $p$-Value P-P Plot