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The number of data points in a calibration curve influences the R-squared (R²) value, which represents the linearity of the curve. More data points generally lead to a higher R² value indicating a better fit and stronger linear relationship between the analyte concentration and the measured signal. However, the number of data points is not the sole factor; the quality and distribution of the data points are also crucial. A calibration curve with a large number of points clustered in a narrow range may still have a lower R² than a curve with fewer points spread across a wider range, if the data is more scattered in the latter case.