In evaluating a new test, which reflects a significant bias caused by constant error?

When evaluating a new test, a significant bias caused by constant error is best reflected by a significant difference in intercept. This type of bias indicates that the test consistently deviates from the true value at a specific level, regardless of the actual concentration or the value being measured.

In practical terms, if a test exhibits a constant error, it means the results are consistently higher or lower than the true values by a fixed amount across the range of measurement. This is often represented graphically in a calibration plot where the line does not pass through the origin or does not match the expected 1:1 line, indicating that there is a systematic shift. This is seen in the significant difference in intercept, where the intercept of the regression line on the graph of test results versus true values is not zero, showing that the test systematically overestimates or underestimates the actual values.

The other options do not specifically indicate constant bias. A high standard deviation would suggest variability in the results but not necessarily a constant error. A low correlation coefficient indicates a weak linear relationship between measurements but does not inherently reflect constant bias. Close results with high error may suggest inconsistency or imprecision but do not directly relate to the constant error that is highlighted by a significant difference in