<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="letter-spacing: -0.15pt;"><span style="font-size: x-small;"><span style="font-family: Batang;">This paper empirically tests for methodological superiority in detecting divergent earnings (the difference between actual and expected earnings).<span style="mso-spacerun: yes;"> </span>Divergent earnings are generated using Value Line forecasted and reported earnings data.<span style="mso-spacerun: yes;"> </span>Two hundred random samples of 100 cases each are drawn.<span style="mso-spacerun: yes;"> </span>One hundred independent two sample tests are performed with 0%, 1%, 3%, 5%, 7%, and 10 % positive earnings introduced.<span style="mso-spacerun: yes;"> </span>The two sample tests are performed using both parametric (t test), and nonparametric (Mann Whitney test) statistics.<span style="mso-spacerun: yes;"> </span>They are performed on the “divergent earnings” data deflated by: 1) forecasted earnings , and 2) the market price of the stock.<span style="mso-spacerun: yes;"> </span>The results indicate that the superior alternative is nonparametric statistical methods based upon ranks, and the deflator choice under these nonparametric methods is of little consequence.</span></span></span></p>