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Randomisation eliminates selection bias.
Randomisation eliminates selection bias: there are, however, other forms of bias we guard against.
One of the most important aspects of random allocation is that it eliminates selection bias.
The rationale is simply that this eliminates selection bias in the calibration.
Random allocation to study groups remains the only method that eliminates selection and confounding biases.
Whilst randomisation eliminates selection bias, there are other sources of bias we need to avoid.
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Our population-based design in the setting of a universal healthcare system largely eliminated selection biases stemming from selective inclusion of specific hospitals, health insurance systems, patients, or age groups.
Second, the random cluster sampling method utilised combined with the high response rate eliminated selection bias.
Using these stated preferences allows us to eliminate selection biases in an analysis to estimate treatment effects.
Nested models were fitted to high-spatiotemporal resolution satellite (GPS) telemetry, controlled for temporal autocorrelation, and eliminated selection bias of pseudo-absences using a semi-non-parametric bootstrap.
We observe that captive-rodent breeding protocols, designed to increase reproductive output, simultaneously exert strong selection against reproductive senescence and virtually eliminate selection that would otherwise favor tumor suppression.
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