The Behrens-Fisher problem in comparing means of two normal populations is revisited. Lee and Gurland (1975) suggested a solution to the problem and provided the set of coefficients required in computing critical values for the case alpha = 0.05, where alpha is the nominal level of significance. This solution, called the Lee-Gurland Test in this article, has proven to be practical as far as calculation is involved, and more importantly, it maintains the actual size very close to alpha = 0.05 for possible values of the ratio of population variances. This merit has not been attained by most of the Behrens-Fisher solutions in the literature. In this article, the coefficients for other values of alpha, namely 0.025, 0.01 and 0.005 are provided for wider applications of the test. Moreover, careful and detailed comparisons' are made in terms of size and power with the other practical solution: the Welch's Approximate t Test. Due to a possible drawback of the Welch's Approximate t Test in controlling the actual size, especially for small alpha and small sample sizes, the Lee-Gurland Test presents itself as a slightly better alternative in testing equality of two normal population means. The coefficients mentioned above are also fitted by the functions of the reciprocals of the degrees of freedom, so that the substantial amount of table-looking can be avoided. Some discussions are also made in regarding the recent ''Welch vs. Gosset'' argument: Should the Student's t Test be dispensed off from the routine use in testing the equality of two normal means?
