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Data Quality Score Measurements
Comprehensive evaluation framework to ensure research data integrity through systematic assessment of sample bias, fraud detection, and data consistency measures.
Sample Bias
Sample bias occurs when certain members of a population are systematically more likely to be selected than others, leading to unrepresentative results. This can happen through various selection methods, participant characteristics, or study design factors that skew the sample away from the true population.
Self-selection bias
People with specific characteristics are more likely to participate than others.
Non-response bias
People who refuse to participate or drop out systematically differ from those who take part.
Under-coverage bias
Some members of a population are inadequately represented in the sample.
Survivorship bias
Successful observations or people are more likely to be represented in the sample than unsuccessful ones.
Pre-screening or advertising bias
Bias due to the way participants are pre-screened or where a study is advertised.
Healthy user bias
Volunteers for preventative interventions are more likely to pursue health-boosting behaviors than others.