Figure 5.

(A) Overestimating the ELP results in a more significant negative prediction error, especially as the lens power (P) is larger (equation = FM). The mean error and the covariance between (E_i) and (P_i² + 2K_iP_i) share the same negative sign, leading to a negative covariance (C < 0). (B) In our simulations, when the ARC is overestimated, the value of P² + 2KP diminishes due to the inverse relationship between R and the corneal power K. Consequently, Haigis's formula predicts a more hyperopic refraction, resulting in a negative prediction error. Both the prediction error E and (P² + 2KP) have the same negative sign, leading to a negative covariance (C < 0). (C) When the refractive index of the cornea (n_k) is overestimated, the value of P² + 2KP rises due to the corresponding increase in K. As a result, the formula predicts a more myopic refraction. The mean error and the covariance between (E_i) and (P_i² + 2K_iP_i) bear the same positive sign, implying that C is negative (C < 0). (D) Overestimating the AL generates a prediction error that becomes more positive as P² + 2KP increases. The mean error and covariance share the same positive sign, resulting in a negative covariance (C < 0).